U.S. patent application number 17/824364 was filed with the patent office on 2022-09-08 for systems and methods for non-contact multiparameter vital signs monitoring, apnea therapy, apnea diagnosis, and snore therapy.
This patent application is currently assigned to ResMed Sensor Technologies Limited. The applicant listed for this patent is ResMed Sensor Technologies Limited. Invention is credited to Scott Tadashi Miyasato, Isar Mostafanezhad, Robert Haruo Nakata, Erik Vossman.
Application Number | 20220280064 17/824364 |
Document ID | / |
Family ID | 1000006359279 |
Filed Date | 2022-09-08 |
United States Patent
Application |
20220280064 |
Kind Code |
A1 |
Nakata; Robert Haruo ; et
al. |
September 8, 2022 |
SYSTEMS AND METHODS FOR NON-CONTACT MULTIPARAMETER VITAL SIGNS
MONITORING, APNEA THERAPY, APNEA DIAGNOSIS, AND SNORE THERAPY
Abstract
Aspects of the of the disclosure relate to a non-contact
physiological motion sensor and a monitor device that can
incorporate use of the Doppler effect. A continuous wave of
electromagnetic radiation can be transmitted toward one or more
subjects and the Doppler-shifted received signals can be digitized
and/or processed subsequently to extract information related to the
cardiopulmonary motion in the one or more subjects. The extracted
information can be used, for example, to determine apneic events
and/or snoring events and/or to provide apnea or snoring therapy to
subjects when used in conjunction with an apnea or snoring therapy
device. In addition, methods of use are disclosed for sway
cancellation, realization of cessation of breath, integration with
multi-parameter patient monitoring systems, providing positive
providing patient identification, or any combination thereof.
Inventors: |
Nakata; Robert Haruo;
(Honolulu, HI) ; Mostafanezhad; Isar; (Honolulu,
HI) ; Miyasato; Scott Tadashi; (Mililani, HI)
; Vossman; Erik; (Honolulu, HI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ResMed Sensor Technologies Limited |
Clonskeagh |
|
IE |
|
|
Assignee: |
ResMed Sensor Technologies
Limited
Clonskeagh
IE
|
Family ID: |
1000006359279 |
Appl. No.: |
17/824364 |
Filed: |
May 25, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16110974 |
Aug 23, 2018 |
11350849 |
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17824364 |
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14208173 |
Mar 13, 2014 |
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16110974 |
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PCT/US2012/055648 |
Sep 14, 2012 |
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14208173 |
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13298248 |
Nov 16, 2011 |
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PCT/US2012/055648 |
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13108795 |
May 16, 2011 |
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13298248 |
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PCT/US11/36543 |
May 13, 2011 |
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13108795 |
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61345065 |
May 14, 2010 |
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61345070 |
May 15, 2010 |
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61370457 |
Aug 4, 2010 |
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61535937 |
Sep 16, 2011 |
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61535943 |
Sep 16, 2011 |
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61535937 |
Sep 16, 2011 |
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61535943 |
Sep 16, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/6831 20130101;
A61N 1/36031 20170801; A61B 5/113 20130101; A61B 5/0816 20130101;
G01S 7/003 20130101; A61B 5/4818 20130101; A61B 5/6823 20130101;
G01S 13/825 20130101; A61N 1/37282 20130101; G01S 7/41 20130101;
A61B 5/112 20130101; A61B 5/4836 20130101; A61B 5/0036 20180801;
A61H 23/02 20130101; G01S 13/50 20130101; A61N 1/3601 20130101;
A61B 5/087 20130101; A61B 5/085 20130101; A61B 5/14551 20130101;
A61H 21/00 20130101; A61B 5/486 20130101; G01S 13/88 20130101; A61B
5/0507 20130101; G01S 13/87 20130101; A61B 5/7257 20130101 |
International
Class: |
A61B 5/08 20060101
A61B005/08; A61H 23/02 20060101 A61H023/02; A61B 5/00 20060101
A61B005/00; A61H 21/00 20060101 A61H021/00; A61N 1/36 20060101
A61N001/36; G01S 7/00 20060101 G01S007/00; G01S 13/50 20060101
G01S013/50; G01S 13/82 20060101 G01S013/82; G01S 13/87 20060101
G01S013/87; G01S 13/88 20060101 G01S013/88; A61B 5/113 20060101
A61B005/113 |
Claims
1. A system for detecting and treating sleep apnea, said system
comprising: one or more non-contact physiological motion sensors
configured to generate a signal; a sensor processing unit
comprising a processor and coupled with the one or more non-contact
physiological motion sensors, the sensor processing unit configured
to process the signal of the one or more non-contact physiological
motion sensors and to extract information related to an apneic or
snoring state of a subject to obtain a signal relating to an apnea
event; the sensor processing unit including a communications
module; and the system further comprising a therapeutic device
configured to communicate with the communications module and
comprising a bio-feedback mechanism configured to stimulate an
anatomical region of the subject when an apnea event is detected,
wherein the therapeutic device is configured to stimulate a nerve
or muscle in the region of a neck that is associated with
breathing.
2. The system of claim 1, wherein the therapeutic device is
configured to noninvasively stimulate the nerve or muscle.
3. The system of claim 2 wherein the therapeutic device is
configured to stimulate a hypoglossal nerve region.
4. The system of claim 1, wherein the therapeutic device comprises
a vibratory stimulation element.
5. The system of claim 1, wherein the therapeutic device comprises
a vibratory stimulus of progressively increasing pulse width and
pulse repetition rate.
6. The system of claim 1, wherein the therapeutic device comprises
a neck patch constructed from biocompatible materials.
7. The system of claim 1, wherein at least one non-contact
physiological motion sensor of the one or more non-contact
physiological motion sensors is radar-based and configured to
measure physiological motion and derive respiratory motion from the
measured physiological motion.
8. The system of claim 1, wherein at least one non-contact
physiological motion sensor of the one or more non-contact
physiological motion sensors is radar based and further configured
to detect non-respiratory motion.
9. The system of claim 1, wherein the one more non-contact
physiological motion sensors includes a sensor configured to:
generate electromagnetic radiation from a source of radiation,
wherein the frequency of the electromagnetic radiation is in the
radio frequency range, transmit the electromagnetic radiation
towards a subject using one or more transmitters, receive a
radiation scattered at least by the subject using one or more
receivers, extract a Doppler shifted signal from the scattered
radiation, and transform the Doppler shifted signal to a digitized
motion signal.
10. The system of claim 9, wherein the digitized motion signal
comprises one or more frames, wherein the one or more frames
include time sampled quadrature values of the digitized motion
signal, and wherein the at least one non-contact physiological
motion sensor and the sensor processing unit are configured to:
demodulate the one or more frames using a demodulation algorithm
executed by one or more processors to isolate a signal
corresponding to a physiological movement of the subject or part of
the subject, analyze the signal to obtain information corresponding
to a non-cardiopulmonary motion or other signal interference, and
process the signal to obtain information corresponding to the
physiological movement of the subject or part of the subject,
substantially separate from the non-cardiopulmonary motion or other
signal interference.
11. The system of claim 1, wherein the at least one non-contact
physiological motion sensor is a radar based sensor.
12. The system of claim 11, wherein the at least one non-contact
physiological motion sensor is further configured to detect a heart
rate of the subject, the heart rate being used to confirm an apnea
indicated by other measurements.
13. The system of claim 12, wherein the at least one non-contact
physiological motion sensor comprises multiple antenna hardware to
track movement of subject while sleeping.
14. A method of treating snoring, comprising the steps of:
detecting a snoring event of a patient via one or more sensors;
sending information regarding the snoring event from the one or
more sensors to a sensor processing unit; sending a command from
the sensor processing unit to an external therapeutic device
operably connected to directly stimulate a hypoglossal nerve region
of the patient, thereby activating the external therapeutic device;
alleviating the snoring event, without awakening the patient, via
the external therapeutic device providing hypoglossal nerve
stimulation of increasing frequency; and ceasing stimulation when
the snoring event has ceased.
15. The method of claim 14, wherein detecting the snoring event of
the patient via one or more sensors employs a snoring detector
comprising one or more selected from the group consisting of a
microphone, acoustic stethoscope, and a transducer configured to
detect the snoring event.
16. The method of claim 15, wherein a therapeutic device comprising
the snoring detector is configured to be coupled with a separate
stand-alone device selected from the group consisting of: a sensor,
a smartphone, and a computer tablet.
17. The method of claim 16, wherein the therapeutic device
comprises a battery having an indicator of battery condition.
18. The method of claim 16, wherein the therapeutic device
comprises one or more selected from the group consisting of:
storage of data, a web interface, a display, user interface and
controls, a clock, recording hardware and software, and
communications hardware and software.
19. The method of claim 16, wherein the therapeutic device
comprises an embedded processor to process sensor signals and to
relay data to the stand-alone device.
20. The method of claim 14, wherein the external therapeutic device
is an external stimulator comprising a vibration transducer and/or
electrodes configured to produce electrical signals to produce
vibrations to stimulate the hypoglossal nerve region of the
patient.
21. The method of claim 14, wherein the external therapeutic device
comprises a neck patch.
22. The method of claim 21, wherein the neck patch comprises a
retention layer including alignment edges to align the neck patch
to the hypoglossal nerve region for therapeutic placement.
23. The method of claim 22, wherein the external therapeutic device
comprises a plurality of vibratory force transfer regions through
which therapeutic energy to stimulate the hypoglossal nerve region
is delivered to the patient.
24. The method of claim 21, wherein the external therapeutic device
comprises a wireless receiver, the wireless receiver configured to
receive a wireless trigger signal from a separate wireless
transceiver, the external therapeutic device providing hypoglossal
nerve region stimulation in response to the wireless trigger
signal.
25. The method of claim 14, wherein at least one of the one or more
sensors is a motion sensor positioned in a location remote to the
patient.
26. The method of claim 14, wherein the one or more sensors are
directly attached to the patient.
Description
PRIORITY CLAIM
[0001] This application is a continuation of International
Application No. PCT/US2012/055648 (Atty. Docket No. KSENS.084P1WO),
filed on Sep. 14, 2012 which claims priority as a
continuation-in-part application of U.S. application Ser. No.
13/298,248 filed on Nov. 16, 2011, which is a continuation-in-part
application of U.S. patent application Ser. No. 13/108,795 filed on
May 16, 2011, which in turn claims priority under 35 U.S.C. .sctn.
119(e) to U.S. Provisional Application No. 61/345,065 (Atty. Docket
No. KAI-00084), filed on May 14, 2010, titled "Integration of
Radar-based Respiratory Measurement or Monitoring with
Multi-parameter Patient Monitoring and/or Multi-parameter Vital
Signs Measurement Systems"; U.S. Provisional Application No.
61/345,070 (Atty. Docket No. KAI-00085), filed on May 15, 2010,
titled "Methods for Sway Cancellation for Non-Contact Measurement
of Cardiopulmonary Motion"; U.S. Provisional Application No.
61/370,457 (Atty. Docket No. KAI-00086), filed on Aug. 4, 2010,
titled "Patient Identification in Conjunction With a Remote Vital
Sign Sensing Radar System"; U.S. patent application Ser. No.
13/108,795 also claims the benefit of priority of International
Application No. PCT/US2011/36543 (Atty. Docket No. KSENS.084WO),
filed on May 13, 2011, titled "Systems and Methods for Non-Contact
Multiparameter Vital Signs Monitoring, Apnea Therapy, Sway
Cancellation, Patient Identification, and Subject Monitoring
Sensors." U.S. application Ser. No. 13/298,248 filed on Nov. 16,
2011 also claims the benefit under 35 U.S.C. .sctn. 119(e) as a
nonprovisional application of U.S. Provisional Application No.
61/535,937 (Atty. Docket No. KAI-000887) filed on Sep. 16, 2011 and
U.S. Provisional Application No. 61/535,943 (Atty. Docket No.
KAI-00088) filed on Sep. 16, 2011. Each of the foregoing priority
applications are hereby incorporated by reference in their
entireties, as well as each of the priority applications cited in
the Application Data Sheet filed herewith.
[0002] This application is also a continuation of International
Application No. PCT/US2012/055648 (Atty. Docket No. KSENS.084P1WO),
filed on Sep. 14, 2012 which also claims the benefit under 35
U.S.C. .sctn. 119(e) as a nonprovisional application of U.S.
Provisional Application No. 61/535,937 (Atty. Docket No.
KAI-000887) filed on Sep. 16, 2011 and U.S. Provisional Application
No. 61/535,943 (Atty. Docket No. KAI-00088) filed on Sep. 16, 2011.
Each of the foregoing priority applications is incorporated herein
by reference in its entirety.
[0003] This application also incorporates by reference in their
entireties all of the following: U.S. application Ser. No.
12/575,447 (Atty. Docket No. KSENS.100CP1), filed on Oct. 7, 2009,
titled "Non-Contact Physiologic Motion Sensors and Methods For
Use;" U.S. application Ser. No. 12/418,518 (Atty. Docket No.
KSENS.100A), filed on Apr. 3, 2009, titled "Non-Contact Physiologic
Motion Sensors and Methods For Use;" U.S. Provisional Application
No. 61/072,983 (Atty. Docket No. KSENS.021PR), filed on Apr. 3,
2008, titled "Doppler Radar System for Local and Remote Respiration
Signals Monitoring"; U.S. Provisional Application No. 61/072,982
(Atty. Docket No. KSENS.023PR), filed on Apr. 3, 2008, titled
"Method for Detection of Cessation of Breathing"; U.S. Provisional
Application No. 61/123,017 (Atty. Docket No. KSENS.024PR), filed on
Apr. 3, 2008, titled "Method for Detection of Motion Interfering
with Respiration"; U.S. Provisional Application No. 61/123,135
(Atty. Docket No. KSENS.025PR), filed on Apr. 3, 2008, titled
"Method for Detection of Presence of Subject"; U.S. Provisional
Application No. 61/125,021 (Atty. Docket No. KSENS.028PR), filed on
Apr. 21, 2008, titled "Non-contact Spirometry with a Doppler
Radar"; U.S. Provisional Application No. 61/125,019 (Atty. Docket
No. KSENS.029PR), filed on Apr. 21, 2008, titled "Monitoring
Physical Activity with a Physiologic Monitor"; U.S. Provisional
Application No. 61/125,018 (Atty. Docket No. KSENS.030PR), filed on
Apr. 21, 2008, titled "Non-contact Method for Calibrating Tidal
Volume Measured with Displacement Sensors"; U.S. Provisional
Application No. 61/125,023 (Atty. Docket No. KSENS.032PR), filed on
Apr. 21, 2008, titled "Use of Empirical Mode Decomposition to
Extract Physiological Signals from Motion Measured with a Doppler
Radar"; U.S. Provisional Application No. 61/125,027 (Atty. Docket
No. KSENS.033PR), filed on Apr. 21, 2008, titled "Use of Direction
of Arrival and Empirical Mode Decomposition Algorithms to Isolate
and Extract Physiological Motion Measured with a Doppler Radar";
U.S. Provisional Application No. 61/125,022 (Atty. Docket No.
KSENS.034PR), filed on Apr. 21, 2008, titled "Data Access
Architectures for Doppler Radar Patient Monitoring Systems"; U.S.
Provisional Application No. 61/125,020 (Atty. Docket No.
KSENS.035PR), filed on Apr. 21, 2008, titled "Use of Direction of
Arrival Algorithms to Isolate and Separate Physiological Motion
Measured with a Doppler Radar"; U.S. Provisional Application No.
61/125,164 (Atty. Docket No. KSENS.036PR), filed on Apr. 22, 2008,
titled "Biometric Signature Collection Using Doppler Radar System";
U.S. Provisional Application No. 61/128,743 (Atty. Docket No.
KSENS.037PR), filed on May 23, 2008, titled "Doppler Radar Based
Vital Signs Spot Checker"; U.S. Provisional Application No.
61/137,519 (Atty. Docket No. KSENS.039PR), filed on Jul. 30, 2008,
titled "Doppler Radar Based Monitoring of Physiological Motion
Using Direction of Arrival"; U.S. Provisional Application No.
61/137,532 (Atty. Docket No. KSENS.040PR), filed on Jul. 30, 2008,
titled "Doppler Radar Respiration Spot Checker with Narrow Bean
Antenna Array"; U.S. Provisional Application No. 61/194,838 (Atty.
Docket No. KSENS.041PR), filed on Sep. 29, 2008, titled "Doppler
Radar-Based Body Worn Respiration Sensor"; U.S. Provisional
Application No. 61/194,836 (Atty. Docket No. KSENS.042PR), filed on
Sep. 29, 2008, titled "Wireless Sleep Monitor Utilizing Non-Contact
Monitoring of Respiration Motion"; U.S. Provisional Application No.
61/194,839 (Atty. Docket No. KSENS.043PR), filed on Sep. 29, 2008,
titled "Continuous Respiratory Rate and Pulse Oximetry Monitoring
System"; U.S. Provisional Application No. 61/194,840 (Atty. Docket
No. KSENS.044PR), filed on Sep. 29, 2008, titled "Separation of
Multiple Targets' Physiological Signals Using Doppler Radar with
DOA Processing"; U.S. Provisional Application No. 61/194,848 (Atty.
Docket No. KSENS.045PR), filed on Sep. 30, 2008, titled "Detection
of Paradoxical Breathing with a Doppler Radar System"; U.S.
Provisional Application No. 61/196,762 (Atty. Docket No.
KSENS.046PR), filed on Oct. 17, 2008, titled "Monitoring of Chronic
Illness Using a Non-contact Respiration Monitor"; U.S. Provisional
Application No. 61/200,761 (Atty. Docket No. KSENS.047PR), filed on
Dec. 2, 2008, titled "Detection of Paradoxical Breathing with a
Paradoxical Breathing Indicator with a Doppler Radar System"; U.S.
Provisional Application No. 61/200,876 (Atty. Docket No.
KSENS.048PR), filed on Dec. 3, 2008, titled "Doppler Radar Based
Monitoring of Physiological Motion Using Direction of Arrival and
An Identification Tag"; U.S. Provisional Application No. 61/141,213
(Atty. Docket No. KSENS.049PR), filed on Dec. 29, 2008, titled "A
Non-Contact Cardiopulmonary Sensor Device for Medical and Security
Applications"; U.S. Provisional Application No. 61/204,881 (Atty.
Docket No. KAI-00050), filed on Jan. 9, 2009, titled "Doppler Radar
Based Continuous Monitoring of Physiological Motion"; U.S.
Provisional Application No. 61/204,880 (Atty. Docket No.
KAI-00051), filed on Jan. 9, 2009, titled "Doppler Radar
Respiration Spot Checker with Narrow Beam Antenna Array"; U.S.
Provisional Application No. 61/206,356 (Atty. Docket No.
KAI-00052), filed on Jan. 30, 2009, titled "Doppler Radar
Respiration Spot Check Device with Narrow Beam Antenna Array: Kai
Sensors Non-Contact Respiratory Rate Spot Check"; U.S. Provisional
Application No. 61/154,176 (Atty. Docket No. KAI-00053), filed on
Feb. 20, 2009, titled "A Non-Contact Cardiopulmonary Monitoring
Device for Medical Imaging System Applications"; U.S. Provisional
Application No. 61/154,728 (Atty. Docket No. KAI-00054), filed on
Feb. 23, 2009, titled "Doppler Radar-Based Measurement of Vital
Signs for Battlefield Triage"; U.S. Provisional Application No.
61/154,732 (Atty. Docket No. KAI-00055), filed on Feb. 23, 2009,
titled "Doppler Radar-Based Measurement of Presence and Vital Signs
of Subjects for Home Healthcare"; U.S. Provisional Application No.
61/178,930 (Atty. Docket No. KAI-00057), filed on May 15, 2009,
titled "Aiming or Aligning Methods and Indicator Display for a
Doppler Radar System;" U.S. Provisional Application No. 61/181,289
(Atty. Docket No. KAI-00058), filed on May 27, 2009, titled
"Intermittent Doppler Radar Respiration Spot Check;" U.S.
Provisional Application No. 61/184,315 (Atty. Docket No.
KAI-00059), filed on Jun. 5, 2009, titled "Doppler Radar
Respiration Spot Check with Automatic Measurement Length;" and U.S.
Provisional Application No. 61/226,707 (Atty. Docket No.
KAI-00060), filed on Jul. 18, 2009, titled "Spiral Antenna for a
Contacting Cardiopulmonary Sensor."
BACKGROUND
I. Field
[0004] This application in general relates to one, two, or more
monitors that can assess the physiological and/or psychological
state of a subject. In particular, some implementations relate to
non-contact and radar-based physiologic sensors and their method of
use that can provide, apnea monitoring, apnea therapy to subjects,
sway cancellation, multi-parameter systems, realize cessation of
breath, identify patients, or any combination thereof.
II. Description of the Related Art
[0005] Motion sensors that can obtain physiological information of
a subject, such as respiratory activity, cardiac activity,
cardiovascular activity, and cardiopulmonary activity on a
continuous or intermittent basis can be useful in various medical
applications. Unfortunately, such physiologic activity often occurs
in the presence of various other motions, such as, for example,
rolling over while sleeping, etc. Thus, data from such motion
sensors can typically include desired components corresponding to
the physiological activity being measured, and undesired components
corresponding to other motions, noise, etc. Some existing systems
do not adequately separate the desired components from the
undesired components.
SUMMARY
[0006] One or more of these and/or other problems can be solved by
a system that uses a radar-based sensor to sense physiological
motion and a processing system that analyzes the data from the
radar to distinguish desired data components corresponding to
various physiological activity from undesired data components due
to other activity, motions, noise, etc. The system can be used to
obtain respiratory rate, heart rate, and physiological waveforms
including, but not limited to, heart waveforms, pulse waveform,
and/or a respiratory waveform. These rates and waveforms can be
analyzed to assess various physiological and medical parameters
such as, for example, respiratory rates, cardiac rates, respiratory
effort, depth of breath, tidal volume, vital signs, medical
conditions, psychological state, or location of the subject, etc.
These waveforms can also be used to synchronize ventilation or
medical imaging with respiratory and/or cardiac motion. The
information in these rates and waveforms can be used in many
embodiments, including vital signs assessments, apnea monitors,
general patient monitoring, neonatal monitoring, burn victim
monitoring, home monitoring of the elderly or disabled, triage,
chronic illness management, post-surgical monitoring, monitoring of
patients during medical imaging scans, disease detection,
assessment of psychological state, psychological or psychiatric
evaluation, pre-resuscitation assessment, post-resuscitation
assessment, and/or lie detection. Various embodiments of the motion
sensors can be used in medical applications in various environments
including, but not limited to, hospitals, clinics, homes, skilled
nursing facilities, assisted living facilities, health kiosks,
emergency rooms, emergency transport, patient transport, disaster
areas, and battlefields. Various embodiments of the motion sensors
can be used for security applications including, but not limited
to, security screening at airports, borders, sporting events and
other public events, or as a lie detector. Various embodiments of
the physiological motion sensors can distinguish valid measurement
of heart and respiratory activity from interference, noise, or
other motion, and it can provide continuous, point in time,
intermittent and/or piecemeal data from which rates, signatures,
and key variations can be recognized. Various embodiments of the
physiological motion sensor can operate with no contact and work at
a distance from a subject. Some embodiments of the physiological
motion sensor can also operate when placed on the subject's chest
in contact with the body. Various embodiments of the physiological
motion sensor can operate on subjects in any position, including
lying down, reclined, sitting, or standing. Various embodiments of
the physiological motion sensor can operate on subjects from
different positions relative to the subject, including from the
subject's, from the subject's side, from the subject's back, from
above the subject, and from below the subject.
[0007] Various embodiments of the motion sensors can operate as an
apnea therapeutic device which may include a wireless or wired
device which can be triggered during an apneic event detected by
the motion sensor to provide a stimulus to the point where the
subject resumes normal breathing without sleep arousal, or
awakening or substantially awakening the patient from sleep.
[0008] Various embodiments of the motion sensor can include a
system comprising two or more vital signs sensors and a processing
unit capable of detecting, estimating and cancelling the subject's
possible sway motion from the subject's vital signs.
[0009] Various embodiments of the motion sensor can implement a
method of detecting apneic events, including, but not limited to,
cessation of breath is based on estimating the relative amplitude
of the respiratory waveform during the times of valid physiological
motion that are more than a certain length of time.
[0010] Various embodiments of the motion sensors can be integrated
into a separate contact based patient monitoring device and/or
contact based vital signs measurement device, that can be further
analyzed to provide other or more detailed vital signs.
[0011] Various embodiments of the motion sensors include one or
more sensors that can be wirelessly connected to a patient
identification device that can be placed on or near the subject
that emits and/or re-emits a signal to provide positive patient
identification.
[0012] In one aspect, a system for treating sleep apnea is
provided. The system can include a wireless sleep monitor. The
wireless sleep monitor can include one or more antennas, with each
of the one or more antennas configured to receive electromagnetic
radiation and/or transmit electromagnetic radiation. The wireless
sleep monitor can also include one or more processors configured to
extract information related to cardiopulmonary motion by executing
at least one of a demodulation module, a non-cardiopulmonary motion
detection module, and a rate estimation module. The one or more
processors can be further configured to detect an apneic event. In
addition, the wireless sleep monitor can include a communications
module configured to communicate with a therapeutic device. The
therapeutic device can be configured to perform at least one action
related to a sleep apnea state of the subject. The wireless sleep
monitor can also include a therapeutic device comprising a
bio-feedback mechanism configured to stimulate the patient in order
to treat an anatomic or physiologic condition associated with
apnea, such as stimulating a nerve and/or muscle, such as the
hypoglossal nerve region in the patient's neck when an apneic event
is detected. The stimulant causes the patient to shift position,
swallow, cough, move the palate or tongue, or restore muscle tone
in the genioglossus muscle in the patient's neck, thereby restoring
the upper airway passage. In some embodiments, the therapeutic
device can be configured to stimulate one or more regions of a
patient's brain to treat central apnea.
[0013] According to another aspect, a system for sensing a
physiological motion is provided. The system can include one or
more sources for generating electromagnetic radiation, wherein the
frequency of the generated electromagnetic radiation is in the
radio frequency range. The system can also include one or more
communications modules configured to perform at least one of the
following: transmit the generated electromagnetic radiation towards
a subject and receive a radiation scattered at least by the
subject. In addition, the system can include one or more antennas,
where each of the one or more antenna is configured to transmit
electromagnetic radiation and/or receive electromagnetic radiation.
The system can further include one or more processors configured
to: extract information related to cardiopulmonary motion by
executing at least one of a demodulation algorithm, a
non-cardiopulmonary motion detection algorithm, a rate estimation
algorithm, a paradoxical breathing algorithm and a direction of
arrival algorithm; analyze the signal to obtain information
corresponding to a non-cardiopulmonary motion or other signal
interference; extract a Doppler shifted signal from the scattered
radiation; and transform the Doppler shifted signal to a digitized
motion signal, said digitized motion signal comprising one or more
frames, wherein the one or more frames comprise time sampled
quadrature values of the digitized motion signal; isolate a signal
corresponding to a physiological movement at least a portion part
of the subject; obtain information corresponding to the
physiological movement of at least a portion of the subject based
on the isolated signal, said information substantially separate
from at least one of said non-cardiopulmonary motion and other
signal interference; and estimate one or more of the group
consisting of: non-contact, spot, interval and continuous vital
signs parameters and communicate the information to an output
system that is configured to perform an output action. The system
can be configured to perform at least one of the following: screen
a sleep disorder, diagnose a sleep disorder, and provides therapy
to the sleep disorder.
[0014] Another aspect is a method for treating sleep apnea. The
method can include detecting, via a wireless sleep monitor, an
apneic event associated with a subject; transmitting information
related to the apneic event to a therapeutic device configured to
stimulate the patient in order to treat an anatomic or physiologic
condition associated with apnea, including a nerve or muscle, such
as the hypoglossal nerve or other nerve in the subject's neck all
without necessarily arousing the patient from sleep but terminating
the apneic event.
[0015] Yet another aspect is a vital-signs monitoring system. The
system can include a first vital sign sensor and a second vital
sign sensor, the second vital sign sensor spaced apart from the
first vital sign sensor, the first vital sign sensor and the second
vital sign sensor comprising one or more antennas configured to
perform one or more of the following: transmit electromagnetic
radiation and receive electromagnetic radiation. In addition, the
system can include one or more processors configured to extract
information related to cardiopulmonary motion by executing at least
one of a demodulation module, a non-cardiopulmonary motion
detection module, and a rate estimation module; wherein the one or
more processors are further configured to cancel the sway motion
associated with a subject and generate a cardiopulmonary signal
associated with the subject.
[0016] Another aspect is a method for detecting, estimating and
cancelling sway motion of a subject from vital sign measurements
associated with the subject. The method can include receiving
signals generated by two or more sensors including at least a first
sensor and a second sensor, wherein the received signals include at
least one of demodulated signals and signals associated with an I
path and a Q path; and performing a linear combination of the
received signals such that signal power associated with the
received signals is substantially minimized.
[0017] In accordance with yet another aspect, a method of detecting
an apneic event is provided. The method can include monitoring an
instantaneous amplitude over time of a respiratory signal by
squaring the respiratory signal and filtering the respiratory
signal via a moving average filter; generating a cumulative
histogram of the instantaneous amplitude; setting one or more
thresholds for a low breathing amplitude based on the cumulative
histogram; determining one or more apneic timespans based on
comparing the instantaneous amplitude to at least one of the one or
more thresholds within the time span associated with valid
physiological motion; and reporting timestamps corresponding to at
least one apneic event.
[0018] According to another aspect, a system for integrated
monitoring of physiological parameters of a subject is provided.
The system can include one or more non-contact vital sign sensors
configured to: generate a signal, such as an electromagnetic
signal, e.g., a radio frequency (RF) signal; transmit the generated
RF signal towards a subject; receive radiation scattered by the
subject; extract a Doppler shifted signal from the scattered
radiation; and derive information corresponding to physiological
movement of at least a portion of the subject that is substantially
separate from non-cardiopulmonary motion. The system can also
include at least one of a separate contact based patient monitoring
device and a separate contact based vital signs measurement
device.
[0019] Yet another aspect is a system for monitoring physiological
signs associated with a subject and positively identifying the
subject. The system can include at least one of a contact based
patient monitoring device, a non-contact based patient monitoring
device, and a vital sign measurement sensor. The system can also
include a patient identification device in communication with at
least one of the contact based patient monitoring device, the
non-contact based patient monitoring device, and the vital sign
measurement sensor.
[0020] In one aspect, another system for detecting and treating
sleep apnea is provided. The system can include one or more of a
sensor, such as, for example, a non-contact radar sensor aimed at
the chest to detect ventilatory effort, a microphone embedded in a
therapeutic device such as an anatomic such as a neck patch sensor
to monitor airflow, a nasal airflow sensor as an auxiliary airflow
monitor, a pulse oximeter sensor to detect oxygen saturation and
heart rate, and/or an accelerometer to detect body motion. One or
more of the sensors can be coupled with a sensor processing unit
that can be worn on the patient's arm or another location that may
detect apneic events. One or more of the sensors may be wired to
the sensor processing unit or may wirelessly communicate to the
sensor processing unit. In addition, the sensor processing unit can
include a communications module configured to communicate with a
therapeutic device. The therapeutic device can be configured to
perform at least one action related to a sleep apnea state of the
subject. The wireless sleep monitor can also include a therapeutic
device comprising a bio-feedback mechanism configured to stimulate
the patient in order to treat an anatomic or physiologic condition
associated with apnea, such as the brain, a nerve or a muscle,
including but not limited to the hypoglossal nerve region in the
patient's neck when an apneic event is detected causing the patient
to shift position, swallow, cough, move the palate or tongue, or
restore muscle tone in the genioglossus muscle in the patient's
neck, thereby restoring the upper airway passage. The sensor
processing unit may detect the end of the apnea event and cease any
electrical signal and mechanical stimulation in the neck patch. The
system may include application software for sleep quality analysis,
e.g., web, PC, tablet, or smartphone-based software.
[0021] In one aspect, another system for diagnosing sleep apnea is
provided. The system can include one or more sensors, such as a
non-contact radar sensor aimed at the chest to detect ventilatory
effort, a nasal airflow sensor as an auxiliary airflow monitor, a
pulse oximeter sensor to detect oxygen saturation and heart rate,
and/or an accelerometer to detect body motion. One or more of the
sensors can be coupled with a sensor processing unit, e.g., worn on
the patient's arm or other location that may detect apneic events.
One or more of the sensors may be wired to the sensor processing
unit or may wirelessly communicate to the sensor processing unit.
The system may include a web based or PC based application software
to assist the clinician in assessing subject's apnea severity by
reporting sleep breathing disorder events and computing and
reporting the AHI (apnea-hypopnea index), event duration, and
timestamps, and/or other patient-related information.
[0022] In one aspect, a system for detecting and treating snoring
is provided. The system can include a sensor, such as an auditory
or vibratory sensor, such as a microphone embedded in the
therapeutic device such as a neck patch sensor to detect snoring
events. The therapeutic device can be configured to perform at
least one action related to detecting and treating a snoring event.
The therapeutic device may comprise a bio-feedback mechanism
configured to stimulate the hypoglossal nerve region in the
patient's neck when a snoring event is detected causing the patient
to shift position, swallow, cough, move the palate or tongue, or
restore muscle tone in the genioglossus muscle in the patient's
neck, thereby restoring the upper airway passage and possibly
terminating the snoring event. The therapeutic device may detect
the end of the snoring event and cease any electrical signal and/or
mechanical stimulation in the therapeutic device. The device may be
coupled with a separate stand-alone device, such as a sensor,
smartphone, or computer tablet with its own display, user interface
and controls, clock, recording hardware and software, and/or
communications hardware and software.
BRIEF DESCRIPTION OF THE DRAWINGS
[0023] FIG. 1A schematically illustrates an embodiment of a
physiological motion sensor system comprising radar.
[0024] FIGS. 1B-1F graphically illustrate measurements obtained by
the system illustrated in FIG. 1A.
[0025] FIG. 2 schematically illustrates a block diagram of a
radar-based physiological motion sensor system integrated with a
remote interface.
[0026] FIG. 3 schematically illustrates a block diagram of a system
including radar-based physiological motion sensor including an
add-on module.
[0027] FIGS. 4A-4B schematically illustrate various embodiments of
a radar-based physiological motion sensor that is configured to
wirelessly communicate with a patient monitor.
[0028] FIG. 5 illustrates a flowchart of an embodiment of a method
configured to perform DC cancellation.
[0029] FIGS. 6A and 6B illustrate flowcharts of embodiments of a
method of performing DC compensation.
[0030] FIG. 6C illustrates the acquired signal fit to a curve or a
line.
[0031] FIG. 6D illustrates a demodulation algorithm utilizing a
circle-find or an arc-find function.
[0032] FIG. 7 illustrates an embodiment of a linear demodulation
algorithm.
[0033] FIG. 8A illustrates an embodiment of an algorithm to assess
the regularity of respiration.
[0034] FIG. 8B illustrates a system configured to determine the
regularity of respiration.
[0035] FIGS. 9A-9D illustrate an embodiment of a method configured
to detect non-cardiopulmonary motion.
[0036] FIGS. 10A-10D illustrate various embodiments of an
identification system configured to provide positive patient
identification in conjunction with remote vital signal sensing.
[0037] FIG. 10E illustrates a system configured to enabling
positive identification using a tag attached to the patient.
[0038] FIG. 10F schematically illustrates an embodiment of a
passive transponder RFID technology.
[0039] FIG. 10G schematically illustrates an embodiment of a
Doppler respiratory and identification reader.
[0040] FIG. 10H illustrates an embodiment of a method of
identification reading and vital signs signals processing of the
sideband signals.
[0041] FIG. 11 illustrates an embodiment of the radar-based
physiological motion sensor comprising a sensor unit, a
computational unit and a display unit.
[0042] FIG. 12 illustrates an embodiment of a method to determine a
paradoxical breathing indicator.
[0043] FIG. 13 illustrates an embodiment of a network topology of a
plurality of clusters that include radar-based physiological motion
sensors.
[0044] FIG. 14A depicts an embodiment of a wireless respiration
sensor configured to measure respiration motion, determine apneic
events and send commands to start and stop stimulation to the
therapeutic device.
[0045] FIG. 14B shows an embodiment of an apnea therapy device and
its components.
[0046] FIG. 14C shows an embodiment of an apnea therapeutic device
configured to detect apneic events and send commands to start and
stop stimulation to the therapeutic device.
[0047] FIG. 14D shows an embodiment of an apnea therapy device and
its components.
[0048] FIG. 15A shows an embodiment of an a system for vital signs
measurement for a standing subject using two Doppler radar
sensors.
[0049] FIG. 15B shows plots of signals acquired from two sensors
that have been processed, yielding physiological motion and
estimated sway signal.
[0050] FIG. 16 graphically illustrates a respiration amplitude
histogram, a cumulative histogram and a threshold point used in
detection of cessation of breath.
[0051] FIG. 17A shows an embodiment of a apnea diagnosis device
configured to detect and collect apneic events in assessing a
subject's apnea-hypopnea index (AHI); FIG. 17B shows an embodiment
of a apnea diagnosis device and its components.
[0052] FIG. 18A shows an embodiment of a snore therapy device
configured to detect snoring events and start and stop stimulation
of the therapy device.
[0053] FIG. 18B shows an embodiment of a sleep therapy device and
its components.
[0054] FIG. 19 shows a drawing of the neck patch which includes the
vibrating motors and a microphone.
[0055] FIGS. 19A-19J schematically illustrate embodiments of a neck
patch including adhesive and non-adhesive layers and an opening to
which a vibratory motor can be attached.
[0056] FIG. 20A show a possible cable breakout for connecting the
optional sensors to the devices.
[0057] FIG. 20B shows the sensor processing unit's enclosure.
DETAILED DESCRIPTION
I. Non-Contact Vital Signs Monitoring
[0058] One embodiment includes a method of sensing motion using a
motion sensor, the method can include generating electromagnetic
radiation from a source of radiation, wherein the frequency of the
electromagnetic radiation is in the radio frequency range,
transmitting the electromagnetic radiation towards a subject using
one or more transmitters, receiving a radiation scattered at least
by the subject using one or more receivers, extracting a Doppler
shifted signal from the scattered radiation, transforming the
Doppler shifted signal to a digitized motion signal, the digitized
motion signal comprising one or more frames, wherein the one or
more frames include time sampled quadrature values of the digitized
motion signal, demodulating the one or more frames using a
demodulation algorithm executed by one or more processors to
isolate a signal corresponding to a physiological movement of the
subject or a part of the subject, analyzing the signal to obtain
information corresponding to a non-cardiopulmonary motion or other
signal interference, processing the signal to obtain information
corresponding to the physiological movement of the subject or a
part of the subject, substantially separate from the
non-cardiopulmonary motion or other signal interference, and
communicating the information to an output system that is
configured to perform an output action.
[0059] In one embodiment, the output system includes a display unit
configured to display the information. In one embodiment, the
output system includes an audible system that is configured to
report information or alerts audibly based on the information. In
one embodiment, the output system includes an external medical
system that is configured to perform an action based on the
information. In one embodiment, the demodulating algorithm includes
a linear demodulation algorithm, an arc-based demodulation
algorithm or a non-linear demodulation algorithm. In one
embodiment, the information is displayed at least alphanumerically,
graphically and as a waveform.
[0060] In various embodiments the demodulating algorithm includes
projecting the signal in a complex plane on a best-fit line,
projecting the signal in a complex plane on a principal
eigenvector, or aligning a signal arc to a best-fit circle and
using the best-fit circle parameters to extract the angular
information from the signal arc.
[0061] In various embodiments demodulating includes computing in
one or more processors a first set of covariance matrices of a
first subset of frames selected from the one or more frames,
determining a first A-matrix, wherein the first A-matrix includes a
weighted sum of the first set of covariance matrices, determining a
first parameter vector corresponding to a first primary value of
the first A matrix, storing the first parameter vector in a memory
device which is in communication with the one or more processors.
In one embodiment, demodulation includes, computing in the one or
more processors a second set of covariance matrices of a second
subset of frames selected from the one or more frames, determining
a second A-matrix, wherein the second A-matrix includes a weighted
sum of the second set of covariance matrices, determining a second
parameter vector corresponding to a second primary value of the
second A-matrix, calculating an inner product of the first
parameter vector and the second parameter vector, multiplying the
second parameter vector by the sign of the inner product, and
projecting the values of the second frame on the second parameter
vector to obtain the demodulated signal. In one embodiment, the
first primary value includes the largest eigenvalue of the first
A-matrix and the first primary vector includes an eigenvector
corresponding to the eigenvalue. In one embodiment, the second
primary value includes the largest eigenvalue of the second
A-matrix and the second primary vector includes an eigenvector
corresponding to the eigenvalue.
[0062] In one embodiment, the source of radiation includes an
oscillator. In one embodiment, the one or more transmitters include
one or more antennae. In one embodiment, the one or more receivers
include one or more antennae or arrays of antennae. In one
embodiment, the transmitting and receiving antennae are the same
antennae. In one embodiment, the receiver includes a homodyne
receiver. In one embodiment, the receiver includes a heterodyne
receiver. In one embodiment, the receiver includes a low-IF
receiver configured to transform the Doppler-shifted signal to a
Doppler-shifted signal comprising frequencies in a low intermediate
frequency range, which is digitized and digitally transformed to a
digitized motion signal.
[0063] In one embodiment, the one or more processors include at
least one of a digital signal processor, a microprocessor and a
computer. In one embodiment, the output system includes a display
unit configured to display information regarding the physiological
movement of a user at a remote location.
[0064] In one embodiment, analyzing the signal includes executing a
non-cardiopulmonary motion detection algorithm configured to detect
the absence of non-cardiopulmonary motion is detected if the signal
includes a single stable source or the presence of
non-cardiopulmonary signal if at least the signal is unstable or at
least the signal has multiple sources.
[0065] In one embodiment, analyzing the signal includes executing a
non-cardiopulmonary motion detection algorithm configured to detect
the presence of non-cardiopulmonary motion if the signal indicates
an excursion larger than the subject's maximum chest excursion from
cardiopulmonary activity.
[0066] In one embodiment, analyzing the signal includes executing a
non-cardiopulmonary motion detection algorithm configured to detect
the presence of non-cardiopulmonary motion if a best-fit vector
related to linear demodulation changes significantly.
[0067] In one embodiment, analyzing the signal includes executing a
non-cardiopulmonary motion detection algorithm configured to detect
the presence of non-cardiopulmonary motion if a RMS difference
between a complex constellation of the signal and a best fit vector
related to linear demodulation changes significantly.
[0068] In one embodiment, analyzing the signal includes executing a
non-cardiopulmonary motion detection algorithm configured to detect
the presence of non-cardiopulmonary motion if an origin or radius
of a best-fit circle related to arc-based demodulation changes
significantly.
[0069] In one embodiment, analyzing the signal includes executing a
non-cardiopulmonary motion detection algorithm configured to detect
the presence of non-cardiopulmonary motion if a RMS difference
between a complex constellation of the signal and a best-fit circle
related to arc-based demodulation changes significantly.
[0070] In one embodiment, analyzing the signal includes executing a
non-cardiopulmonary motion detection algorithm by one or more
processors to detect the presence or absence of non-cardiopulmonary
motion or other signal interference from the digitized motion
signal, wherein the non-cardiopulmonary motion detection algorithm
includes a first mode which detects a presence of
non-cardiopulmonary motion or other signal interference and a
second mode which detects a cessation of non-cardiopulmonary motion
or other signal interference.
[0071] In one embodiment, the first mode includes selecting a first
subset of frames from the one or more frames and computing in the
one or more processors a first set of covariance matrices of the
first subset of frames filtered by a low-pass filter, determining a
first A-matrix wherein the A-matrix includes a weighted sum of the
first set of covariance matrices, determining a first parameter
vector corresponding to a first primary value of the first A
matrix, storing the first parameter vector in a memory device which
is in communication with the one or more processors. One embodiment
further includes computing in the one or more processors a second
set of covariance matrices of a second subset of frames filtered by
the low-pass filter, determining a second A-matrix, wherein the
A-matrix includes a weighted sum value of the second set of
covariance matrices, determining a first and a second primary value
of the second A-matrix, determining a second parameter vector
corresponding to the first primary value of the second A-matrix,
calculating an inner product of the first parameter vector and the
second parameter vector, calculating a ratio of the first primary
value of the second A matrix to the second primary value of the
second A matrix, calculating a first energy corresponding to the
average energy of a third subset of frames filtered by a high-pass
filter and a second energy corresponding to the average energy of a
fourth subset of frames filtered by a high-pass filter, and
calculating a ratio of the second energy to the first energy. In
one embodiment, the first primary value includes the largest
eigenvalue of the first A-matrix and the first primary vector
includes an eigenvector corresponding to the eigenvalue. In one
embodiment, the first primary value of the second A-matrix includes
the second largest eigenvalue of the second A-matrix, the second
primary value of the second A-matrix includes the largest
eigenvalue of the second A-matrix and the second primary vector of
the second A-matrix includes an eigenvector corresponding to the
first primary value of the second A-matrix.
[0072] One embodiment includes computing in the one or more
processors a first condition, the first condition being the inner
product is less than a first threshold value or the ratio of the
first primary value of the second A matrix to the second primary
value of the second A matrix is less than a second threshold value
or the ratio of the second energy to the first energy is greater
than a third threshold value, wherein the presence of
non-cardiopulmonary motion or other signal interference is detected
if the first condition is true and the ratio of the second energy
to the first energy is greater than a fourth threshold value. In
one embodiment, the first threshold value is approximately between
0.6 and 1. In one embodiment, the second threshold value is
approximately between 4 and 12. In one embodiment, the third
threshold value is approximately between 4 and 20. In one
embodiment, the fourth threshold value is approximately between 0.1
and 0.8.
[0073] In one embodiment, the second mode includes selecting in the
one or more processors each and every consecutive subset of frames
within a fifth subset of frames, computing in the one or more
processors covariance matrices for every subset of frames computing
in the one or more processors an A'-matrix for each subset of
frames, wherein the A'-matrix is the weighted average of the
covariance matrices in the subset, computing in the one or more
processors a rho-matrix, wherein each element of the rho-matrix
corresponds to a first primary vector of the corresponding
A'-matrix, computing the inner product of each pair of primary
vectors in the rho-matrix and selecting a minimum absolute value of
the inner products, calculating an A matrix which is the sum of the
covariance matrices in a sixth subset of frames, determining the
first primary value of the A-matrix and the second primary value of
the A matrix, calculating the ratio of the first primary value of
the A matrix to the second primary value of the A matrix,
[0074] One embodiment includes computing in the one or more
processors a second condition, the second condition being the
minimum absolute value of the inner products is greater than a
first threshold value and the ratio of the first primary value to
the second primary value is greater than a second threshold value,
wherein the cessation of non-cardiopulmonary motion or other signal
interference is detected if the second condition is true. In one
embodiment, the fifth threshold value is approximately between 0.6
and 1. In one embodiment, the sixth threshold value is
approximately between 4 and 12. In one embodiment, the first
primary vector includes an eigenvector corresponding to the largest
eigenvalue of the corresponding A'-matrix. In one embodiment, the
first primary value includes the largest eigenvalue of the A-matrix
and the second primary value includes the second largest eigenvalue
of the A-matrix. One embodiment includes computing a frame from the
one or more frames when the non-cardiopulmonary motion
substantially ceased. In one embodiment, one or more frames
preceding the frame are discarded.
[0075] One embodiment includes a method of estimating the rate of a
physiological motion using a motion sensor, generating an
electromagnetic radiation from a source of radiation, wherein the
frequency of the electromagnetic radiation is in the radio
frequency range, transmitting the electromagnetic radiation towards
a subject using one or more transmitters, receiving a radiation
scattered at least by the subject using one or more receivers,
extracting a Doppler shifted signal from the scattered radiation,
transforming and digitizing the Doppler shifted signal to a
digitized motion signal, the digitized motion signal comprising one
or more frames, wherein the one or more frames include time sampled
quadrature values of the digitized motion signal, demodulating the
one or more frames using a demodulation algorithm executed by one
or more processors to isolate a signal corresponding to a
physiological movement of the subject or a part of the subject,
executing a non-cardiopulmonary motion detection algorithm by the
one or more processors to identify from the digitized motion signal
one or more non-cardiopulmonary motion detection events or other
signal interference events corresponding to the presence or absence
of a non-cardiopulmonary motion or other signal interference,
executing by one or more processors a rate estimation algorithm to
estimate a rate of the physiological movement, and providing
information related to at least the rate of the physiological
movement of the subject or a part of the subject to an output unit
that is configured to output the information.
[0076] In one embodiment, the rate estimation algorithm includes
collecting a plurality of samples from the demodulated frames,
identifying one or more samples from the plurality of samples
corresponding to non-cardiopulmonary motion detection events and
setting to zero the one or more samples from the plurality of
samples to obtain at least a first subset of the plurality of
samples, and subtracting in the one or more processors a mean of
the first subset from the first subset. One embodiment includes
calculating in the one or more processors a Fourier transform of
the samples included in the first subset to obtain a magnitude
spectrum of the samples in the first subset. In one embodiment, the
estimated frequency domain rate of the physiological movement
corresponds to the largest magnitude component in the spectrum of
the samples in the first subset. One embodiment includes
identifying either at least three positive zero crossings or at
least three negative zero crossings in the first subset,
identifying at least a first value for the samples within a first
and a second zero crossing, the first value being the largest
magnitude positive value or largest magnitude negative value,
identifying at least a second value for the samples within a second
and a third zero crossing, the second value being the largest
magnitude positive value or largest magnitude negative value
comparing the first and second values against a threshold value,
identifying at least a first breathing event if the first value is
greater than a threshold value, identifying at least a second
breathing event if the second value is greater than a threshold
value, and estimating a time domain respiration rate based on at
least the first and second breathing events and the time interval
between the first, second and third zero crossings. One embodiment
includes calculating in the one or more processors a Fourier
transform of the samples included in the first subset to obtain a
magnitude spectrum of the samples in the first subset, estimating a
frequency domain respiration rate of the physiological movement
that corresponds to the largest magnitude spectrum of the samples
in the first subset, and comparing the time domain rate and the
frequency domain rate to verify an accuracy of the time domain rate
and the frequency domain rate.
[0077] In one embodiment, the rate estimation algorithm includes
identifying at least three consecutive peaks from the plurality of
samples, such that a valley is included between two consecutive
peaks, and determining a respiration rate based on a number of
consecutive peaks detected and the time interval between a first
and a last peak.
[0078] In one embodiment, the rate estimation algorithm includes
identifying at least three consecutive valleys from the plurality
of samples, such that a peak is included between two consecutive
valleys, and determining a respiration rate based on a number of
consecutive valleys detected and the time interval between a first
and a last valley. In one embodiment, the rate algorithm selects
whether to identify peaks or valleys depending on which occurs
first. In one embodiment, the rate estimation algorithm averages
the respiration rate based on a number of consecutive peaks and the
respiration rate based on a number of consecutive valleys to
improve the robustness of the rate estimate.
[0079] FIG. 1A shows a physiological motion sensor system 100
wherein a radar 101 senses motion and/or physiologic activity of a
subject 102. Data from the radar 101 is provided to a processing
system 103 configured to analyze the radar data to determine
various desired physiological parameters and provide output
information regarding the physiological parameters to an output
system and/or device configured to perform an output action. In
various embodiments, the output device can include a display system
configured to display an audible system configured to report
information or issue alerts or a medical device configured to
perform a function based on the information. The system 100 can
further include a communications system configured to communicate
using wired and/or wireless communication links. The communications
system can use standard or proprietary protocols. FIG. 1B shows an
example of a measurement obtained by the system 100 as displayed on
a display unit.
[0080] FIGS. 1B-1F illustrate examples of measurements obtained by
the system 100. The measurements can include waveforms due to
cardiopulmonary activity of a subject 102 displayed on a display
unit.
[0081] FIG. 1B illustrates the waveforms obtained by embodiments of
the system 100 described above for a 54-year-old male subject with
a body mass index (BMI) of 23 with Hypertension and Congestive
Heart Failure. Plot 104 of FIG. 1B shows the physiological motion
signal (e.g., respiratory rate and the amplitude of respiration)
detected by the radar-based physiological motion sensor system.
Plot 105 illustrates the physiological motion signal detected by a
conventional contact physiological motion sensor (e.g., a chest
strap). Plot 106 shows the comparison between the normalized motion
signal detected by the radar-based physiological motion sensor and
the normalized conventional sensor. Plot 106 shows good
correspondence between the two signals.
[0082] FIG. 1C illustrates variations in the respiratory rate and
the amplitude of respiration obtained by embodiments of the system
100 described above for a 44-year-old male with a BMI of 40, with
Diabetes, Hypertension, and CAD. Plot 107 of FIG. 1C shows the
physiological motion signal (e.g., respiratory rate and the
amplitude of respiration) detected by the radar-based physiological
motion sensor system. Plot 108 illustrates the physiological motion
signal detected by a conventional contact physiological motion
sensor (e.g., a chest strap). Plot 109 shows the comparison between
the normalized motion signal detected by the radar-based
physiological motion sensor and the normalized conventional sensor.
Like the plot 106 shown in FIG. 1B, plot 109 shows good
correspondence between the two signals.
[0083] FIG. 1D illustrates the physiological motion signal obtained
by an embodiment of the system 100 for a 55-year-old male with a
BMI of 40, with High Cholesterol, Hypertension, and CAD, while he
was snoring. Plot 110 shows the motion signal detected by the
radar-based physiological motion sensor and illustrates detection
of apnea (cessation of breathing) and variation in the respiration
signal baseline. Plot 111 is a corresponding measurement obtained
by a conventional monitor while plot 112 illustrates the comparison
between the conventional monitor and the system 100.
[0084] FIG. 1E illustrates the physiological motion signal obtained
by an embodiment of the system 100 for a 59-year-old female with a
BMI of 30, with COPD and CHF. Plot 113 shows the measurement
obtained by the physiological motion sensor of system 100. Plot 114
shows the corresponding measurement obtained by a conventional
sensor and plot 115 shows the comparison between the two
measurements.
[0085] FIG. 1F illustrates the physiological motion signal obtained
by an embodiment of the system 100 for a 57-year-old Female with a
BMI of 38, with CHF and CAD. Plot 116 illustrates detection of
apnea (cessation of breathing) and variation in the respiration
signal baseline for the subject. Plot 117 illustrates a
corresponding measurement obtained by a conventional sensor and
plot 118 shows the comparison between the two.
[0086] In various embodiments, the radar-based physiological sensor
can include a user interface to allow a user to enter information
or to allow the user to enter commands and/or instructions. In
various embodiments, the user interface can include a start button
and a stop button. In various embodiments, the user interface can
include a clear button. In various embodiments, the user interface
can include additional buttons (e.g., a save button, a print
button, etc.) or a keypad.
[0087] In various embodiments, the system 100 can communicate the
information to a remote display and/or a central server or one or
more computing devices. In some embodiments, SOAP web service can
communicate data to one or more computing devices, such as a
server. From the one or more computing devices, the respiration
data can be accessed by a remote client with a browser and a
network connection, such as an internet connection. FIG. 2
illustrates a block diagram of a system integrated with a remote
interface 200. The system illustrated in FIG. 2 includes a
radar-based physiological sensor 201 in electrical communication
with a signal processor 202. The information from the signal
processor can be displayed locally on a local display 203 or can be
stored in a server 205 over a web service 204. A remote client 207
can access the information stored on the server using a network,
such as the internet 206, or another communication protocol.
[0088] In various embodiments, the system 100 can include an add-on
module with wireless connectivity. FIG. 3 illustrates a block
diagram of a system 300 including radar-based physiological sensor
including an add-on module. As illustrated in FIG. 3, the device
301 is networked to a patient monitoring system 302 using a
personal area network technology such as Bluetooth, Ultra Wide
Band, Wireless USB, etc. The patient monitoring system 302 can
display the cardiopulmonary motion information on its local
interface and/or forward the data to a remote database over a
network, such as the internet 304 or a hospital network 303, such
that the information can be accessed by a remote client 305.
[0089] In various embodiments, the continuous vital signs monitor
can also be used in a skilled nursing facility, in a similar
embodiment to the hospital monitor. Embodiments of this device can
be used for general vital signs monitoring of the elderly or ill,
and can also be used for early detection of pneumonia. Embodiments
of the continuous vital signs monitor can also be used in emergency
vehicles (e.g., ambulances, helicopters, etc.) to monitor a patient
during emergency transport. Various embodiments of the system 100
can also determine the duration of subject activity or the
percentage of time the subject is active. This information can be
used to provide an activity index. Changes in the activity index
can be used as indicators of a change in health state. In various
embodiments, the physiological motion sensor can be used to detect
battlefield survivors and monitor their physiological signals. In
various embodiments, a software based array configuration that is
executable by one or more processors can be applied to Doppler
radar to search for survivors in detecting mode, and to track them
in target mode by focusing the beam. Survivor location can be
determined from DOA processing at dual or multiple frequencies.
[0090] As described in more detail below, the system 100 can
implement, which can include storing computer-executable
instructions in non-transitory memory, algorithms for calculating
respiratory rate, accuracy of the respiratory rate, algorithms to
recognize inaccurate data, to recognize interfering motion, to
recognize electrical signal interference, to recognize electrical
noise, to report varying rates, to analyze the regularity or
irregularity of the respiratory rate and to signal or alert a user
if the respiratory rate is high or low, etc.
[0091] As described in more detail below, the system 100 can
include hardware and/or software which is executable by one or more
processors to improve signal quality, such as, for example, RF
leakage cancellation, DC cancellation, noise cancellation, low IF
architecture, homodyne system balancing, etc. Various embodiments
of the system 100 described herein can have the capability to
discern between cardiopulmonary and other motions. In various
embodiments of the system 100, methods and algorithms for motion
discrimination and detection can enable increased accuracy of
cardiopulmonary data. Various embodiments described herein employ
methods of decreasing the delay between the occurrence of an event
and the reporting and display of that event by DC cancellation and
high speed data acquisition. A low time delay can typically be
desirable for applications in which another device uses the
reported event to initiate and/or trigger another action. A low
time delay can also improve synchronization with other
measurements. The respiration and/or heart waveforms that are
generated by the various embodiments described herein can be used
to trigger actions by other systems. For example, various
embodiments relate to triggering medical imaging (e.g., with CT or
MRI scans) based on cardiac or respiratory displacement and/or
triggering assistive ventilation based on spontaneous respiratory
effort. The respiration or heart waveforms that are generated by
the various embodiments described herein can be used to provide
physiological synchronization with other systems. For example,
various embodiments relate to synchronizing cardiopulmonary motion
and/or other motion to medical imaging (e.g., CT scans or MM)
systems, assistive ventilation systems, polygraph systems, security
screening systems, biofeedback systems, chronic disease management
systems, exercise equipment, or any combination thereof.
[0092] Various embodiments of the system 100 can automatically,
using any combination of features of the algorithms related to
Direction of Arrival (DOA), track a subject's physiological signals
as the subject moves around, e.g., up and down in a bed. Various
embodiments of the system 100 can automatically, using any
combination of features of the algorithms related to DOA, track a
subject's location as the subject moves around, e.g., up and down
in a bed. Various embodiments of the system 100 can be configured
to cancel extraneous motion when extracting cardiopulmonary motion
which can result in greater accuracy of the readings. Various
embodiments of the system 100 can also, using algorithms such as
DOA, separate and monitor or measure secondary or multiple
cardiopulmonary motion sources (e.g., cardiopulmonary motion of a
second or multiple subjects nearby can be reported simultaneously).
Various embodiments of the system 100 can also, using algorithms
such as DOA, separate and suppress secondary or multiple
cardiopulmonary motion sources (e.g., cardiopulmonary motion of a
second or multiple subjects nearby can be suppressed such that only
the intended subject is measured). Various embodiments of the
system 100 can include a radio frequency identification (RFID) tag
in conjunction with DOA to enable tracking of the desired
subject.
[0093] Various embodiments described herein can implement various
approaches for motion compensation such as empirical mode
decomposition (EMD), suppression of secondary motion sources with
direction of arrival (DOA) processing, blind signal separation
(BSS), independent component analysis (ICA), suppression of motion
in the direction of high-frequency received signals, or any
combination thereof.
[0094] Various embodiments of the system 100 can include radio
frequency identification (RFID) tag configured to enable positive
identification of a monitored subject. Various embodiments of the
system 100 can be adapted to have various sizes, form factors and
physical dimensions suitable for including in a bedside unit, a
hand held unit, in a PDA, in a smart phone, in a tablet computer, a
module as part of larger medical system, etc. Various embodiments
of the system 100 can include one or more outputs such that
information can be viewed and controlled either locally or
remotely. In various embodiments, the system 100 can be a thin
client application such that the system 100 can include the sensor,
data acquisition, and communications, and demodulation, processing,
and output systems would be in another device. For example, in some
embodiments, the system 100 can be provided to a network system
where controls and processing are centralized for a network of
sensors and the sensor and networking/communications part is
onsite, near the subject. In some embodiments, the system 100 can
automate the initiation of measurements under certain predefined
circumstances, e.g., when person is detected in a room, at set time
intervals, etc. In various embodiments, the system 100 can be used
to perform non-contact measurement of depth of breath and relative
tidal volume or absolute tidal volume. Various embodiments of the
system 100 can be used as a cardiopulmonary and/or activity
monitor.
[0095] In various embodiments of the system 100, the signal
conditioning does not include high-pass filtering, DC-blocking or
DC-cancellation hardware, and the DC offsets are acquired along
with the signal, and removed in software. In some embodiments, a
two-operation method can be used to suppress the DC component in a
signal, in which the first operation concerns the removal of the
static DC offset due to the circuit, while the second operation
addresses the suppression of the time-varying DC offset due to the
clutter, temperature and other factors. In some embodiments, in the
first operation, an estimate of the DC offset is determined by
various methods including, but not limited to, using the value of
the first sample acquired, the mean of the first few samples, or
the mean of the first frame. In other embodiments, the DC offset
can be measured during calibration at the factory, and this factory
value can be subtracted from each frame. In some embodiments, the
estimated DC offset is subtracted from the signal prior to
demodulation. In some embodiments utilizing quadrature receivers,
different values can be calculated and subtracted for each
quadrature channel. In some embodiments, the same DC offset can be
subtracted from every sample and/or every frame of the signal. In
some embodiments utilizing frame-based processing, the second
operation can deduce and suppress a DC estimate from every
demodulated frame by using the value of the first sample in the
frame or the mean of the samples in the frame and suppressing the
DC offset by subtracting this value from that frame before further
processing. In some embodiments, a band-limited signal can be
reconstructed from the zero-mean frames by compensating for the
discontinuity across consecutive frames. In some embodiments, the
discontinuity compensation uses the last sample of the previous
frame and the first sample from the current frame, and then adds a
constant value to the samples in the current frame such that the
difference between the values of the samples specified earlier is
close to zero. In some embodiments, the second operation can apply
a high-pass filter to the signal after it has been conditioned with
the coarse estimate of the DC offset subtraction in the first
operation. In some embodiments, the high pass filter can be applied
to the signal prior to demodulation; in other embodiments, the
high-pass filter can be applied to the signal after demodulation.
In various embodiments, the cut-off frequency of the high-pass
filter can be adjusted to meet signal requirements. In some
embodiments, this cut off frequency can be between approximately
0.01 Hz and 0.1 Hz. In some embodiments, the high-pass filter
cutoff can be determined adaptively, such that it is as high as
suitable for a given respiratory rate. In various embodiments, the
high pass filter can be implemented either as a finite impulse
response filter (FIR) or an infinite impulse response filter
(IIR).
[0096] An embodiment of a method for DC compensation is shown in
FIG. 6A. As illustrated in FIG. 6A, the DC-coupled signal can have
the mean suppressed as shown in block 810, and then high-pass
filtered as shown in block 812 to generate an AC-coupled
signal.
[0097] In some embodiments, high-pass filtering the signal can be
optional and, instead of high-pass filtering, the signal fitted
line or curve can be subtracted. FIG. 6B illustrates a flow chart
of an embodiment of a method for DC compensation in which high-pass
filtering is optional. In the method illustrated by FIG. 6A, a
curve-fitting or line-fitting and subtraction algorithm can be used
with a preset amount of recorded data. In various embodiments, the
duration of the recorded data can be 15 seconds, 30 seconds, 60
seconds or some other duration. The method can comprise fitting the
raw signal, or the signal after the rough DC estimate is removed,
or the signal after high-pass filtering to a line or curve as shown
in block 814. The fitted line can be subtracted from the signal,
removing the slowly-varying DC offset to obtain a fit-subtraction
signal. In various embodiments, this fit-subtraction can be
obtained before demodulation, and can be applied to the I and Q
signals individually. In some other embodiments, this
fit-subtraction can be obtained after demodulation. In some
embodiments, the signal can be fit to a line as shown by trace 816
of FIG. 6C. In some embodiments, the signal can be fit to a
quadratic polynomial or parametric curve, as shown by trace 818 of
FIG. 6C.
[0098] In some embodiments, demodulation can involve an
arctangent-based demodulation algorithm utilizing a circle-find or
arc-find function, which can provide a center and/or a radius as
shown in FIG. 6D. In some embodiments utilizing arctangent-based
demodulation, the center can be used as the reference point and
used to find the phase change generated as an object moves back and
forth in space. In some embodiments, the movement of the arc-center
can be tracked over time. In some embodiments, the tracked center
over time can be fit to a curve which is subtracted in 2
dimensions. In some embodiments, the path can be interpolated
between time tracked center key points. In some embodiments, the
change in the radius can be tracked over time. In some embodiments,
DC offset compensation such as, but not limited to, AC coupling,
first sample subtraction, mean value subtraction, or any
combination thereof can be utilized after arc-tangent demodulation.
In some embodiments, the tracking circle-find algorithm is used
instead of another DC offset compensation method. In various
embodiments, center-tracking can replace the first operation, the
second operation or the first and second operations of the
previously described two-operation DC-offset compensation
algorithm.
[0099] In the system 100, deviation of the phase can be
proportional to the chest motion divided by the wavelength of the
carrier signal, and the amplitude of the signal may not be
significantly affected by chest motion, such that when the phase is
plotted in the I/Q plane, the I/Q constellation is distributed
along an arc of a circle or a full circle. In embodiments in which
the chest motion is small compared to the signal's wavelength, the
arc can sweep a small portion of the circle, such that it can be
approximated by a line, and the phase can be demodulated through
linear methods. Alternatively, if the chest motion is large
compared with the carrier signal's wavelength, the I/Q
constellation samples can be distributed on a larger arc that
cannot be approximated by a line. In some embodiments in which the
transceiver operates at approximately 5.8 GHz, when the chest
motion due to the respiration is approximately 0.5 cm, the phase
deviation due to the chest motion can be approximately 70.degree.;
a 70.degree. arc may not be accurately approximated as a line in
the complex constellation. In these embodiments, non-linear
demodulation based on arctangent function can extract phase
information directly from arc-distributed samples.
[0100] In various embodiments, the quadrature signals can be
demodulated using any of several algorithms, including but not
limited to linear demodulation, arc-based demodulation algorithm
(e.g., arc-tangent demodulation with center tracking), non-linear
demodulation algorithm, or any combination thereof. Demodulation
algorithms can include any of the following methods, but not
limited to, projecting the signal in the complex plane on a
best-fit line, projecting the signal in the complex plane on the
principal eigenvector, aligning the signal arc to a best-fit circle
and using the circle parameters to extract angular information from
the signal arc, or any combination thereof. Linear demodulation can
use any of many algorithms, including projecting the signal in the
complex plane on the principal eigenvector, projecting the signal
on the best-fit line, or any combination thereof. Arctangent
demodulation can extract phase information which is corresponding
to the chest motion associated with cardiopulmonary activity as
described herein. In quadrature systems, data collected by two
orthogonal channels (e.g., In-phase (I) and quadrature phase (Q))
can lie on a circle centered at a DC vector of the channels. After
tracking center vector of the corresponding circle and subtracting
it from the data samples, phase information of received signal can
be extracted through an arctangent function.
[0101] In some embodiments, linear demodulation is the projection
of the signal on a linear vector. In some embodiments, the signal
can be rotated until a maximal projection on the x or y plane is
achieved. In some embodiments, a best fit line can be estimated,
and the data can be projected on the best-fit line. In some
embodiments, specific key points, such as the end points of an arc,
can be connected to form a line, and the signal can be projected on
this line. In some embodiments, the signal can be projected on the
line that provides the most variance in the signal.
[0102] In some embodiments, the hardware can be used in conjunction
with the software to enable linear demodulation. In some
embodiments, the carrier radio frequency can be adjusted with a
phase-locked-loop and/or another method to put one of the channels
in the null, such that most of the signal is on the other channel;
the signal in the non-null channel is used. In some embodiments, a
phase-shifter in the RF circuit can be tuned to a point where one
channel is in the null, and the signal on the other channel can be
used.
[0103] An embodiment of a linear demodulation algorithm is further
described below and illustrated in FIG. 7. In one embodiment, the
algorithm comprises computing covariance matrices for a subset of
input frames as shown in block 901a including the most recent frame
and projecting the data on a primary vector or an eigenvector of
said covariance matrix as shown in block 902. If it is determined
that the current eigenvector is in a reverse direction as compared
to a previously determined eigenvector then the algorithm can
rotate the current eigenvector by 180 degrees.
[0104] In various embodiments, the linear demodulation algorithm
can comprise one or more of the following operations: [0105] 1.
Compute covariance matrix C.sub.M-1 of the current input frame x as
shown in block 901a. [0106] 2. Based on C.sub.M-1 and covariance
matrices C.sub.0 to C.sub.M-2 of previous frames, compute an
A-matrix as shown in block 901b represented by the equation:
[0106] A = i = 0 M - 1 e - .alpha. ( M - 1 - i ) .times. C i
##EQU00001## [0107] In this equation, a can correspond to a damping
factor and can be a positive real number. In various embodiments,
the value of a can range from approximately 0.1 to approximately
0.5. In one embodiment, a can be approximately 0.2. M can
correspond to the number of frames in the buffer and can range from
about 2 to 15. In one embodiment, M can be 10. [0108] 3. Find the
primary vector or eigenvector v.sub.0 corresponding to the largest
primary value or eigenvalue of A as shown in block 901c. [0109] 4.
Compute the inner product of v.sub.0 and v.sub.1, where v.sub.1 can
represent the eigenvector found in operation 3 when performing the
algorithm for the previous input frame as shown in block 901d.
[0110] 5. Multiply v.sub.0 by the sign of the inner product found
in operation 4 as shown in block 901e. [0111] 6. Project samples of
the current input frame x on the eigenvector v.sub.0 calculated in
operation 5 to get the demodulated frame as shown in block 902.
[0112] If a target's periodic physiological motion variation is
represented by x(t), and the wavelength of the radar signal is
represented by .lamda., the quadrature baseband output, assuming
balanced channels, can be expressed as:
B .function. ( t ) = A r .times. exp .function. ( i * ( .theta. + 4
.times. .pi..DELTA. .times. x .function. ( t ) .lamda. ) ) + DC
##EQU00002##
[0113] In this equation, DC can be a complex number representing
the non-time-varying voltage values of the I and Q channels,
.theta. can represent the constant phase shift due to the
transceiver architecture and target range, and Ar can represent the
amplitude of the baseband signal. From (1), it will be appreciated
that if DC, which can come from clutter, intra-circuit reflection,
and self-mixing is estimated and removed, the angle deviation,
which can be linearly proportional to actual physical motion of a
target x(t), can be extracted simply by the arctangent function.
However, if the low-frequency or direct-current component of the
phase shift caused by x(t) is removed, or if DC is not removed,
arctangent demodulation can be more complicated and is not
straightforward.
[0114] In some embodiments, the arc can be segmented (divided into
sections), and the intersection of the perpendicular vectors of the
sections is used to give an estimate of the center using a least
mean square error, maximum likelihood estimation, or other method.
In some embodiments, the end points of an arc can define a chord of
a circle, and the normal vector at the midpoint of the chord can be
defined as the perpendicular axis of the arc; segments along the
arc each have a normal vector, which intersects the arc's
perpendicular axis at the center point. In some embodiments, the
mean, midpoint or median of the intersect points along the
perpendicular axis can be defined as the center of the arc. In some
embodiments, intersection outliers along the axis can be removed
before the center-estimation algorithm is applied. In some
embodiments, a line fit can be performed to find the perpendicular
axis of the arc, which intersects the midpoint between the end
points.
[0115] In some embodiments where the carrier wavelength is shorter
than the displacement of the chest, such that a complete circle is
formed in the I/Q plane, the center can be found by a best fit
circle, center of mass, geometrical center, 2D low-pass filter with
peak-finding, look-up table fitting the data to a variety of
circles, or any combination thereof.
[0116] In some embodiments, demodulation can be performed in
real-time as the center is estimated. In some embodiments,
demodulation can be performed retrospectively for an optimal center
from a built up buffer in memory. In some embodiments, the center
can be tracked periodically over time and fit to a line, quadratic
curve, geometric shape, polynomial interpolation, or any
combination thereof and used as moving center during
demodulation.
[0117] An example of a non-cardiopulmonary motion detection
algorithm is further described below and illustrated in FIGS.
9A-9D. The algorithm can be executed by one or more processors and
can detect non-cardiopulmonary motion and/or other signal
interference by looking at the change in direction of the
eigenvectors, the ratio of the eigenvalues and the change of energy
in the signal, as shown in block 1201b. As illustrated in FIG. 9A,
the algorithm can start in mode 1, as shown in block 1201a, by
assuming that no non-cardiopulmonary motion and/or other signal
interference is present and can switch to mode 2 as shown in block
1201c in response to detecting any non-cardiopulmonary motion
and/or other signal interference. When in mode 2, the algorithm can
similarly check the change in direction of the eigenvectors and the
ratio of eigenvalues, as shown in block 1201a to determine if the
non-cardiopulmonary motion and/or other signal interference has
ceased. If motion ceases, then the algorithm can find the earliest
time (the retrospect) with no motion, as shown in block 1201e. The
algorithm can comprise one or more of the following operations:
[0118] 1. Mode=1 [0119] a. Compute covariance matrix C.sub.M-1 of
the current input frame x.sub.h2 filtered with a first filter
having a filter function h2, as shown in block 1201f of FIG. 9B. In
some embodiments, the first filter can be a low-pass filter. [0120]
b. Using C.sub.M-1 and the covariance matrices C.sub.0 to C.sub.M-2
of previous frames, compute an A-matrix
[0120] A = i = 0 M - 1 C i M , ##EQU00003##
as shown in block 1201g of FIG. 9B, where M can represent the
number of preceding frames to consider and in some embodiments M
can be 32. In various embodiments M can be larger or smaller than
32. [0121] c. Find the eigenvector v.sub.0 corresponding to the
largest eigenvalue of A, as shown in block 1201h of FIG. 9B. [0122]
d. Compute the absolute value chd of the inner product of v.sub.0
and v.sub.1, where v.sub.1 is the eigenvector found in operation c
when performing the algorithm for the previous input frame, as
shown in block 1201i of FIG. 9B. [0123] e. Compute the ratio pc of
the largest to the second-largest eigenvalue, as shown in block
1201j of FIG. 9B. [0124] f. Compute the energy e.sub.1 of the input
frame x.sub.3 filtered with a second filter having a filter
function h3. In various embodiments, the second filter can be a
high-pass filter, as shown in block 1201k of FIG. 9B. [0125] g.
Compute the average energy per frame e.sub.2 of all M-1 previous
input frames x.sub.3 filtered with h3, as shown in block 1201l of
FIG. 9B. [0126] h. Compute the ratio detectp=e.sub.1/e.sub.2, as
shown in block 1201m of FIG. 9B. [0127] i. If (chd<th1 OR
pc<thev1 OR detectp>thp1) AND detectp>thp1d), as shown in
block 1201b and 1201c then non-cardiopulmonary motion or other
signal interference is detected, switch to Mode=2. In various
embodiments th1 can have a value between approximately 0.6 and
approximately 1. In various embodiments, thev1 can have a value in
the ranging from about 4 to 12. In various embodiments, thp1 can
have a value ranging from about 4 to 20. In various embodiments,
thp1d can have a value between approximately 0.1 and approximately
0.8.
[0128] 2. Mode=2 [0129] a. Calculate an A'-matrix represented by
the equation
[0129] A m , n = i = m n C i n - m + 1 , ##EQU00004##
where C.sub.i can represent a covariance matrix from frame i (frame
n being the most recent), as shown in block 1201n of FIG. 9C.
[0130] b. Compute a matrix p of eigenvectors as follows, as shown
in block 1201p of FIG. 9C:
TABLE-US-00001 [0130] For j = 0 To SeqM { For i = 0 To SeqM { i. m
= M - (minM + i - 1) ii. n = M - j iii. .rho..sub.i,j = v.sub.m,n }
}
.rho. = [ v M - ( minM - 1 ) , M - 1 v M - ( minM - 1 ) , M - SeqM
v M - ( minM - SeqM - 1 ) , M - 1 v M - ( minM - SeqM - 1 ) , M -
SeqM ] , ##EQU00005##
where SeqM can be about 5 in some embodiments and can correspond to
the number of preceding frames to consider, where minM can
represent the number of frames prior to current frame to consider
and can be about 8 in some embodiments, where v.sub.m,n can
represent the eigenvector corresponding to the largest eigenvalue
of A.sub.m,n. [0131] c. Compute the ratio pc.sub.i,M-1 of the
largest to the second largest eigenvalue of the matrix A.sub.i,M-1,
as shown in block 1201q of FIG. 9C. [0132] d. Find the minimum chd
of the absolute value of the inner product of all pairs of
v.sub.m,n in .rho., as shown in block 1201r of FIG. 9C. [0133] e.
Compute the energy
ratio.sigma..sub.i=.SIGMA..sub.k=0.sup.Nx.sub.h3.sup.i(k)/.SIGMA..sub.j=i-
.sup.M-1.SIGMA..sub.k=0.sup.Nx.sub.h3.sup.j(k), where
x.sub.h3.sup.i(k) can represent sample k from frame i filtered with
h3, as shown in block 1201s of FIG. 9D. [0134] f. If (chd>th2
AND pc.sub.M-(minM-1),M-1>thev2) then non-cardiopulmonary motion
and/or other signal interference is indicated to have stopped,
switch to Mode=1, as shown in blocks 1201d and 1201e of FIG. 9A. In
various embodiments, th2 can have a value between approximately 0.6
and approximately 1. In various embodiments, thev2 can have a value
between approximately 4 and approximately 12. [0135] g. Retrospect:
Compute 4 indices idx1, idx2, idx3, idx4 as follows, as shown in
block 1201t. [0136] idx1: the largest i such that
V.sub.M.sup.H-(minM-1),M-1V.sub.i,M-1<th3. [0137] idx2: the
largest i such that V.sub.M.sup.H-(minM-1),M-2V.sub.i,M-1<th3.
[0138] idx3: the largest i such that pc.sub.i,M-1<thev2. [0139]
idx4: the largest i such that .sigma..sub.i<thp2. [0140] In
various embodiments, th3 can have a value between approximately 0.6
and approximately 1. In various embodiments, thp2 can have a value
between approximately 4 and 12. In one embodiment, thp2 can be
approximately 5. In one embodiment, th3 can be approximately 0.97.
[0141] h. Then, non-cardiopulmonary motion and/or other signal
interference is indicated to have stopped during frame index
max(idx1, idx2, idx3, idx4), as shown in block 1201u.
[0142] In various embodiments, empirical mode decomposition (EMD)
algorithms can be used to isolate the signal from motion, including
motion due to, but not limited to, non-cardiopulmonary motion by
the subject, cardiopulmonary motion of one or more people other
than the intended subject, non-cardiopulmonary motion of another
person or other people, motion of other objects in the environment,
motion of the radar system, or any combination thereof.
[0143] An example configuration includes a system 100 configured to
operate at a radio frequency of approximately 5.8 GHz with a
direct-conversion receiver and DC-offset cancellation. In various
embodiments, the system 100 includes a single antenna to transmit
radiation and a single antenna to receive radiation. In various
embodiments, one or more antennas can be used to transmit and/or
receive signals. In various embodiments, the system 100 can include
one or more processors configured to execute an arc demodulation
algorithm. In some of these embodiments, the one or more processors
can execute computer-readable instructions stored in non-transitory
memory to perform the algorithm.
II. Apnea Therapy Device
[0144] In various embodiments, the physiological motion sensor can
include a non-contact vital signs monitoring device, such as a
radar-based device that can be configured to detect paradoxical
breathing (e.g., when the abdomen contracts as the rib cage expands
and/or when the rib cage contracts as the abdomen expands). In some
cases, during obstructive apnea paradoxical breathing can be
exhibited, although paradoxical breathing may not indicate an
airway obstruction. In various embodiments, an indication of
paradoxical breathing and of the level of paradoxical breathing can
be useful in detecting obstructive apnea. While the following
description may be described with reference to apnea for
illustrative purposes, any of the principles and advantages can be
applied in connection with detecting, generating alarms, and/or
performing other actions related to any non-respiration and/or
reduced respiration event, as appropriate. For example, any
combination of features described with reference to apnea can be
applied to hypopnea or any other respiratory condition or breathing
pattern, some examples of which are disclosed herein.
[0145] In various embodiments, the system 100 can be configured to
detect the presence of or the degree of paradoxical breathing,
which is a signature of obstructed breathing, respiratory muscle
weakness, respiratory failure, or any combination thereof. The
system (e.g., a continuous monitor, quadrature continuous-wave
Doppler radar system) can monitor the degree of paradoxical
breathing based on analysis of the shape of the complex
constellation and/or the trace of the plot of the in-phase (I) vs.
quadrature (Q) signals from the quadrature radar receiver. An
embodiment of a method to determine a paradoxical breathing
indicator is illustrated in FIG. 28 and includes one or more of the
following operations: [0146] 1. The paradoxical factor can be
estimated by multiplying the ratio of the biggest eigenvalue to the
second biggest eigenvalue by the ratio of the maximum peak-to-peak
value of the signal projected on the principal eigenvector to the
maximum peak to peak value of the signal projected on the vector
orthogonal to the principal vector, as illustrated in block 2801.
[0147] 2. The paradox index can be calculated as a cost function
performed on the paradoxical factor. [0148] 3. If the paradox index
is compared with one or more thresholds, it can be interpreted as
the absence or presence of paradoxical breathing or the degree of
asynchronous respiration.
[0149] In various embodiments, a wireless home sleep monitor
including one, two, or more sensors, e.g., a radar-based
physiological motion sensor can be used as a sleep apnea
therapeutic device as an alternative or in addition to other
therapeutic devices, as shown in FIG. 14A. The home sleep monitor
system can include a sensor and/or monitor configured to detect the
apneic events and trigger a separate device (e.g., a module). In
one embodiments, the system can be configured to detect a period of
apnea, paradoxical breathing, or other parameter that occurs for
about or at least about 5 seconds, 7 seconds, 10 seconds, 12
seconds, 15 seconds, 20 seconds, 30 seconds, 45 seconds, 60
seconds, or more in duration. The separate device can include but
is not limited to, an audible alarm that can increase in volume,
and/or wristwatch, pillow, mattress, clothing items, collars, neck
patches, or any combination thereof that can vibrate with
increasing intensity and/or electric shock, and/or light sources
that flicker with intensity, as show in FIG. 14B. Unlike
conventional alarms that are configured to alert a third party,
such as a physician, nurse, or other healthcare provider of an
apneic, hypopneic, or other adverse respiratory event, alarms in
accordance with some embodiments herein are configured to stimulate
the patient in order to treat an anatomic or physiologic condition
associated with apnea, without necessarily arousing a patient from
sleep. One goal is to stimulate the patient in order to treat an
anatomic or physiologic condition associated with apnea, such as,
e.g., stimulating the hypoglossal nerve region or other nerve
region in the subject's neck to restore muscle tone to the
genioglossus nerve or other nerve, thereby restoring the upper
airway passage of the subject until the subject resumes normal
breathing, without affecting or substantially affecting the
subject's sleep architecture or without arousing the subject from
sleep. In some embodiments, the therapeutic device can invasively
or noninvasively, or directly or indirectly stimulate the
diaphragm, intercostal muscles, accessory muscles, the brain
(including the inspiratory, expiratory, pneumotaxic, and apneustic
centers in the brain for example, and the medulla and pons areas of
the brainstem), hypoglossal, glossopharyngeal, or vagus nerves.
Thereafter, the home sleep monitoring system can send a command to
the therapeutic device to stop stimulation and return to its idle
or normal state until the next apneic event.
[0150] Not to be limited by theory, certain mechanisms describing
potential mechanisms of respiratory physiology and sleep apnea will
now be described. In some embodiments, one, two, or more
therapeutic devices can be utilized to stimulate one, two, or more
of the anatomical and/or physiologic systems described below.
[0151] During the day, stimulus from the descending reticular
activating system creates the drive to breathe. Signals from
chemoreceptors are combined in the brain stem to give a regular,
rhythmic respiratory pattern. Reduced pressure levels of carbon
dioxide (PCO2) during sleep are thought to inhibit ventilation.
PCO2 at or below a certain level halts ventilation and causes
apnea. An increase in alpha activity can be detected on an EEG
indicating a lightening of sleep. Arousals can be associated with
direct activation of the sympathetic nervous system creating an
increase of adrenaline and noradrenaline in the blood. This
lightened sleep stimulates wakeful control mechanisms stimulating
ventilation. Rising PCO2 will cause a quick ventilatory response to
correct arterial blood chemistry after being detected by medullary
chemoreceptors. Breath-to-breath changes in extracellular fluid pH
occur at these chemoreceptors and could influence respiratory
center output. Low responses to chemical stimuli may result in
respiratory pauses during sleep. High chemoresponsiveness produces
respiration instability leading to cycling.
[0152] Chemoreceptors in blood vessels can quickly detect short
term changes in arterial carbon dioxide tension (PaCO2), blood pH,
and arterial oxygen tension (PaO2). Chemoreceptors in the brain are
thought to be located at the ventral surface of the medulla
oblongata, behind the blood brain barrier. Signals from these
chemoreceptors are transferred to the respiratory center in the
brain stem where they are processed and sent by efferent nerves to
the respiratory muscles. One slow, deep breath can be enough to
start an episode of periodic breathing; long respirations create
slow chemical responses and changes blood pressure tensions.
[0153] The genioglossus tongue muscle plays an important role as a
respiratory muscle by maintaining an open airspace for breathing.
Signals sent to the hypoglossal motor nucleus have a specific
effect on the genioglossus muscle. Suppression of the genioglossus
muscle occurs during REM sleep because inhibitory neurotransmitters
block hypoglossal muscle output to the genioglossus muscle causing
the airway to narrow and carbon dioxide to build up in the blood.
When an apnea occurs, gas exchange in the lungs is reduced causing
a rapid decrease in PO2 and a subsequent rise in PCO2. The arousal
that follows increases the respiratory drive exceeding the levels
required to normalize breathing. This arousal driven breathing is a
reflex response that further perpetuates periodic breathing. The
buildup of CO2 in the blood causes reflex stimulation of
genioglossus activity. Gamma-Amniobutyric Acid (GABA) is the main
inhibitory neurotransmitter for the central nervous system with
receptors throughout the medulla. Stimulating the GABA receptors at
the hypoglossal motor nucleus suppresses hypoglossal motor output
enough to diminish muscle tone.
[0154] Central sleep apnea will result if there is a malfunction in
the neurons that control breathing during sleep. Nerve-signaling
chemicals (neurotransmitters) send signals to nerve cells in the
brain to control our sleep-wake cycles. Arousals from sleep have
been associated with direct activation of the sympathetic nervous
system (SNS). The SNS regulates pulse, blood pressure, and change
in muscle tone. When PaO2 levels fall, the SNS alerts the brain to
awaken the person enough to tighten the airway muscles, opening the
trachea.
[0155] Norepinephrine is the primary neurotransmitter for the
postganglionic sympathetic nervous system. Sleep apnea activates
the sympathetic nervous system causing the release of
norepinephrine. As blood oxygen saturation decreases there is an
increased release of norepinephrine.
[0156] In the light stages of sleep periodic breathing is present
and rarely found during REM sleep for most sleep apnea patients.
During REM sleep, signals enter the base of the brain at the pons
and travel to the thalamus, which relays signals to the outer
cerebral cortex, which is responsible for learning and organizing
information. Signals from the pons also shut off neurons in the
spinal cord, temporarily paralyzing the muscles in the limbs.
[0157] Sleep-disordered breathing results from a combination of
factors affecting upper airway patency and the control of
ventilation. Although positive airway pressure therapy is the
primary treatment for patients with moderate to severe obstructive
sleep apnea syndrome, poor compliance and/or refusal is an issue in
up to 40-50% of these patients. Alternatives to positive airway
pressure therapy include mandibular repositioning appliances or
surgical procedures that treat either soft tissue (resection,
repositioning, or stiffening) or bony anatomy. Both modalities aim
to correct specific anatomic abnormalities that may play a role in
upper airway narrowing and collapse during sleep. Although the
mechanisms underlying upper airway collapse are incompletely
understood, there is clearly a decline in pharyngeal neuromuscular
activity during sleep compared to wakefulness in obstructive sleep
apnea patients. Thus, stimulation of upper airway muscles can be
effective.
[0158] Various upper airway dilator muscles, especially the
genioglossus, play a role in maintaining upper airway patency
during sleep. The tensor veli palatini is one possible stimulation
target; others include electrical stimulation of upper airway
musculature to cause tonic and reflexive activation of the
genioglossus muscle during wake and sleep. Consequently, methods
have been explored to stimulate selectively upper airway dilator
muscles, particularly the genioglossus.
Stimulation Frequency
[0159] In some embodiments, stimulation frequency, amplitude and
pulse duration should be great enough to produce tetanic
contraction of the muscle. In some embodiments, a stimulation
frequency of >30 Hz could be used for this purpose. Thereafter,
increases in frequency, amplitude, or pulse duration all produce
progressively increasing levels of muscle recruitment. Muscle force
is almost maximal at stimulation frequencies above 50 Hz.
Therefore, in some embodiments, the frequency to obtain maximal
airway opening could be between 25 Hz and 100 Hz, such as between
50 and 100 Hz, or at least about 25 Hz, 30 Hz, 35 Hz, 40 Hz, 45 Hz,
50 Hz, 60 Hz, 75 Hz, 100 Hz, or more. In some cases, there has been
found to be a frequency-dependent effect of continuous stimulation
on upper airway function.
Stimulation Amplitude
[0160] In some embodiments, fine wire electrodes or submental
stimulation with large amplitudes (10-20 V), a frequency of 50 Hz,
and a pulse duration of 0.2 m sec can be utilized. They found that
the amplitude needed to induce EEG arousal from sleep was
significantly higher than that producing barely tolerable sensation
during wakefulness. In some embodiments, high stimulation
amplitudes up to 10V, 15V, 20 V, 30 V, 40V, 50V, or more can be
used for transcutaneous submental stimulation or intraoral
stimulation, or between about 15-40 V. In various implementations,
one or more sensory stimulating elements, such as a vibratory motor
can be used to stimulate the patient in order to treat an anatomic
or physiologic condition associated with apnea. The vibratory motor
can have variable vibration amplitudes, displacements, and
frequencies. In various embodiments, the frequency of vibration can
be, for example, between about 40 Hz and 400 Hz, between about 100
Hz and 300 Hz, or around 220 Hz. Other parameters (e.g. amplitude,
displacement, etc.) of the vibratory motor can be adjusted based on
the required submental stimulation or stimulation of another
anatomical region depending on the desired clinical result.
Pulse Duration
[0161] When stimulating skeletal muscles directly, in some
embodiments, a marked increase in muscle tension can be obtained
with increasing pulse duration to a range of 0.2-1.0 msec, or less
than 1.0 msec, 0.8 msec, 0.6 msec, 0.4 msec, 0.2 msec, or less in
some embodiments. Pulse duration is limited in some embodiments by
the fact that longer pulse duration typically causes
discomfort.
Timing of the Stimulation with Respect to the Respiratory
Pattern
[0162] In some embodiments, an apnea-demand type stimulator was
used for timing of surface submental stimulation. Stimulation can
begin, for example, 5, 10, 15, or more seconds after apnea onset
and switched off when airflow resumed or after 5 sec, 10 sec, or 15
sec, whichever came first. No stimulation was applied during
periods of decreased airflow (hypopneas), and the timing of
stimulation was not dependent on the respiratory cycle. In some
embodiments, surface submental stimulation is applied during
apneas. In some embodiments, different phases of the respiratory
cycle are stimulated with submental and intraoral electrodes,
including during obstructive events or before the onset of events.
In some embodiments, intramuscular stimulation at the onset of
inspiration can be used to obtain an improvement in airflow. Not to
be limited by theory, airflow can return to baseline at offset of
stimulation, and there is no hysteresis effect on the upper airway,
requiring stimulation with the onset of each inspiratory
effort.
Prevention of Awakening
[0163] Not to be limited by theory, when patients awaken during
stimulation, the observed increase in airflow may be attributed to
a generalized activation of the pharyngeal muscles rather than to
an isolated recruitment of the stimulated muscle. In some
embodiments, hypoglossal nerve stimulation is less likely to
produce awakening or sleep interruption because the nerve is pure
motor, as opposed to the sensory stimulation associated with direct
intramuscular stimulation.
[0164] Apnea affects a large percentage of the population, and it
would be desirable to monitor as well as treat apnea without
surgery or cumbersome devices attached to the subject's body. In
various embodiments, a wireless home sleep apnea therapeutic device
can provide a more comfortable and/or attractive alternative to
those currently on the market (e.g., surgery, oral appliance,
various positive pressure devices via face masks or nostril masks
with headbands, CPaP and BiPap), which can require bulky,
uncomfortable, and/or noisy equipment. These removable devices
result in discomfort to the subject and eventual lack of use by the
subject, and surgery presents a risk due to the implant system.
Thus, there is a need for improved treatment to apnea that can
address the discomfort to the existing approaches. This wireless
monitor can combine radar-based, non-contact measurement of
respiratory effort and may contain other components, such as pulse
oximeter(s), nasal or oral airflow sensor(s), acoustic stethoscopes
or microphones, chest and abdomen sensors with wired or wireless
communications, operating with or without wires on the patient and
with or without minimal contact to the patient. In various
embodiments, the pulse oximeter, nasal or oral airflow sensor(s),
acoustic stethoscopes or microphones, and/or chest and abdomen
sensors can be configured to independently send their data wired or
wirelessly to the hub. This can provide an advantage over other
commercially available home sleep monitors, which require bulky,
uncomfortable, and/or noisy CPAP or Bi-PAP type of device.
[0165] In various embodiments, it is possible to measure
respiratory motion without any contact to the subject with a
radar-based system specifically configured to measure physiological
motion, and respiratory motion can be derived from the
physiological motion signal. In addition to detecting respiratory
rates from the motion, respiratory motion can provide a measure of
respiratory effort, similar to that provided by chest belts
designed to measure respiratory effort. Measurements of respiratory
effort can be useful in determining whether an event is a central
apnea or an obstructive apnea. Respiratory motion can be measured
with a radar-based system overnight, with the subject in any
position in the bed.
[0166] In various embodiments, the radar-based device or chest and
abdomen sensors can be configured to detect paradoxical breathing,
when the abdomen contracts as the rib cage expands and/or when the
rib cage contracts as the abdomen expands. During obstructive
apnea, typically there is paradoxical breathing, although
paradoxical breathing does not necessarily indicate an airway
obstruction. An indication of paradoxical breathing and/or of the
level of paradoxical breathing can be useful in detecting
obstructive apnea.
[0167] In various embodiments, the radar-based device can also
measure motion that is not due to respiration, which can indicate
activity such as tossing and turning in bed, wakefulness,
involuntary movement during sleep, the like, or any combination
thereof. The quality of sleep can be estimated based on level of
activity, and the level of activity can be helpful in determining
the sleep state of the subject. The radar-based device can also be
used to determine when the person is in the bed or out of the bed
and/or to track how often the subject is getting out of bed during
the night.
[0168] In some embodiments, the radar-based device may be
configured to generate data related to a number of physiological
parameters. For example, the radar-based device can generate data
used to measure and/or generate alarms. In various embodiments, the
radar-based device may also measure the heart rate. During apneic
events, the heart rate can increase substantially, and the heart
rate can be used to confirm an apnea that is indicated by other
measurements. This can provide a higher confidence level that an
apnea event has been detected. In various embodiments, the
radar-based device can generate and/or display an indicator of a
confidence level of detecting an apneic event.
[0169] In various embodiments, the radar-based device may be used
to estimate the tidal volume, or the amount of air inhaled and
exhaled with each breath. When the tidal volume is accurately
measured, the tidal volume can be used to estimate the airflow.
[0170] In various embodiments, the radar-based device may include
multiple-antenna hardware and software such that it can track the
subject as he/she moves in bed during the night. This can provide
information about how much the subject is moving within the bed,
and can improve the radar-based measurement of respiration and
activity.
[0171] In various embodiments, the radar-based device may be used
in conjunction with one or more other sensors to provide a more
complete picture of respiration during sleep. Additional sensors
may include but are not limited to the pulse oximeter, nasal or
oral airflow sensor(s), acoustic stethoscopes or microphones,
and/or chest and abdomen sensors
[0172] In various embodiments, the nasal/oral airflow sensor,
acoustic stethoscope and/or microphone can provide an indication of
whether the patient is breathing and/or, with a more advanced
sensor, an estimate of the velocity of the airflow. This can be
used to accurately detect apnea, and with the more advanced
sensors, also detect hypopnea (reduction in airflow). An accurate
measurement of airflow can be useful in determining whether an
event is a hypopnea or an apnea. The nasal/oral airflow sensor may
include one or more thermistors, hot-wire anemometers, pressure
sensors, the like, or any combination thereof. For example, there
may be more than one when the airflow in each nostril and/or at the
mouth are measured independently. It may be difficult to determine
whether an apnea is central or obstructive from only a single
airflow sensor.
[0173] In various embodiments, the pulse oximeter can provide
information on the effectiveness of respiration by arterial
hemoglobin saturation, an estimate of blood oxygenation. Decreases
in blood oxygenation can indicate the severity of an apneic and/or
hypopneic event, and can be clinically significant. The pulse
oximeter can also provide a heart rate measurement. Pulse oximetry
data can be obtained from sensors on the finger or on the ear, but
the finger measurements are generally considered more accurate.
[0174] In various embodiments, the pulse oximeter, nasal or oral
airflow sensor(s), acoustic stethoscopes or microphones, and/or
chest and abdomen sensors contact the patient, but in accordance
with a number of embodiments described herein the pulse oximeter,
nasal or oral airflow sensor(s), acoustic stethoscopes or
microphones, and/or chest and abdomen sensors can advantageously
transmit data wired or wirelessly to the data recording device.
This recording device may be integrated with the radar-based
device.
[0175] In various embodiments, this wireless home sleep monitor,
including the radar-based device, pulse oximeter, nasal or oral
airflow sensor(s), acoustic stethoscopes or microphones, and/or
chest and abdomen sensors operating wired or wirelessly and with
minimal contact to the patient, can provide a detailed picture of
respiration during sleep including measurements related to:
airflow, respiratory effort, and oxygenation. It can also provide
measurements related to one or more of the following: the heart
rate, variability in the heart rate, and information about motion
during sleep. The pulse oximeter, nasal or oral airflow sensor(s),
acoustic stethoscopes or microphones, and/or chest and abdomen
sensors can independently send their data wired or wirelessly to
the hub. This can provide a significant advantages over other
commercially available home sleep monitors, which require wires to
the recording device or wires to a single body-worn device with
then wirelessly transmits data to the recording device.
[0176] In various embodiments, the system can include one or more
of a non-contact radar sensor aimed at the chest to detect
ventilatory effort, a microphone embedded in the therapeutic device
such as a sensor, e.g., a neck patch sensor to monitor airflow, a
nasal airflow sensor such as an auxiliary airflow monitor, a pulse
oximeter sensor to detect oxygen saturation and heart rate, and/or
an accelerometer to detect body motion. FIG. 14C describes a system
with a wireless radar sensor 1902, airflow sensor 1904, pulse ox
sensor 1910, and therapeutic device neck patch 1906 all attached
wired or wirelessly to the sensor `processing unit 1908 with
optional viewing through, for example, a tablet PC, PC, or mobile
phone 1912. One or more of the sensors can be coupled with a sensor
processing unit 1908 worn on the patient's arm or other location
that may detect apneic events. One or more of the sensors may be
wired to the sensor processing unit 1908 through possible cable
breakouts as shown in FIG. 20A, or may wirelessly communicate to
the sensor processing unit 1908. In addition, the sensor processing
unit 1908 can include a communications module configured to
communicate with a therapeutic device 1906. The therapeutic device
1906 can be configured to perform at least one action related to a
sleep apnea state of the subject. The wireless sleep monitor can
also include a therapeutic device 1906 comprising a bio-feedback
mechanism configured to stimulate the patient in order to treat an
anatomic or physiologic condition associated with apnea, e.g., the
hypoglossal nerve region in the patient's neck when an apneic event
is detected causing the patient to shift position, swallow, cough,
move the palate or tongue, or restore muscle tone in the
genioglossus muscle in the patient's neck, thereby restoring the
upper airway passage. The sensor processing unit 1908 may detect
the end of the apnea event and cease any electrical signal and
mechanical stimulation in the neck patch 1906. The system may
include a web based or PC based, for example application software
1940 for sleep quality analysis.
[0177] In various embodiments, the system can include one or more
of an acoustic stethoscope or airflow sensor able to detect
respiration, airflow and/or respiration rate, contact chest and
abdomen sensor or sensors able to detect ventilatory effort,
paradoxical breathing, and/or respiration rate, and/or strain gauge
or other sensing technology such as PVDF to detect movement in
response to stimulation.
[0178] In various embodiments, the thresholds to detect an apnea
event on the sensor processing unit 1908 may be set by the
manufacturer, hospital, healthcare practitioner, or subject.
[0179] In various embodiments, the sensor and/or neck patch 1906
may be capacitively coupled to automatically power on when placed
in use.
[0180] In various embodiments, the neck patch 1906 may include a
rechargeable or replaceable battery which may include a blinking
light or annunciation of the battery condition.
[0181] In various embodiments, the neck patch 1906 and sensor may
include storage of data or a web interface.
[0182] In various embodiments, the sensor may include its own
display, user interface and controls, clock, recording hardware and
software, and/or communications hardware and software.
[0183] In various embodiments, the neck patch 1906 and/or sensor
may be coupled with a smartphone or computer tablet which may
include its own display, user interface and controls, clock,
recording hardware and software, and/or communications hardware and
software.
[0184] In various embodiments, the device may include an embedded
processor to process the signals and control the inter-sensor
communications to relay data to the stand alone devices, such as a
sensor, smartphone, or computer tablet.
[0185] In various embodiments, the neck patch 1906 may be in
disposable form.
[0186] In various embodiments, the device may include a web based
or PC based application software 1940 to assist the clinician in
assessing subject's apnea severity by reporting sleep breathing
disorder events and computing and reporting the AHI, event
duration, and timestamps.
[0187] In various embodiments, the sensor processing unit 1908 may
be housed in an enclosure worn on the arm or other location and
integrated into the neck patch 1906, as shown in FIG. 20B. The
sensor processing unit 1908 may include the CPU 1918, memory 1920
to store the respiratory waveforms and events, power management
circuitry, rechargeable battery 1924, pulse oximetry processor
1926, a transceiver 1922 to communicate with and collect data from
external sensors, and/or a wired connector and cabling 1938 to
collect sensor data, as shown in FIG. 14D.
[0188] In various embodiments, the anatomical, e.g., neck patch
1906 is constructed from biocompatible materials, including a
replaceable substrate with a biocompatible adhesive. The substrate
may have an opening for a semi-rigid vibration plate attached to
the motor, permitting direct contact of the vibration plate to the
skin. The substrate may also have an additional opening permitting
the microphone 1932 to be in close proximity to the skin near the
larynx. The substrate may include indentations to accommodate the
neck patch, sensor processing unit 1908, battery 1934, microphone
1932 and motor 1928. The neck patch cover encloses the components
and substrate. The neck patch is shown in FIG. 19.
[0189] FIGS. 19A-19J illustrate various embodiments of a neck patch
1906 including a first layer and a second layer, e.g., adhesive and
non-adhesive portions and, one, two, or more openings 1950, such as
a central opening to which a sensory stimulating element, such as a
vibratory motor 1960 can be attached. Motor 1960 could have
frequency, amplitude, displacement, and other parameters as
described elsewhere herein. The neck patch 1906 can comprise one,
two, or more layers of foam, cloth or other biocompatible material.
The opening 1950 in the layers of the neck patch 1906 can be used,
for example, for retention of the vibratory motor 1960 such that
the vibratory motor 1960 can be adhered to the neck region or some
other anatomical region to stimulate one, two, or more nerves,
muscles, or other structures associated with respiration or the
airway, for example. The vibratory motor 1960 can be attached to
the opening 1950 by various methods including but not limited to
flanges, complementary locking mechanisms, an interference fit,
adhesives, or mechanical methods such as a clip 1952, which could
be horseshoe-shaped as shown in some embodiments. The adhesive
portion, e.g., an inner layer of the neck patch 1906 can be used to
secure the neck patch 1906 to the appropriate body part. The neck
patch 1906 can have alignment edges 1954a and 1954b that allow the
patient to align the neck patch 1906 to the appropriate anatomical
region for optimum therapeutic or comfort placement. In some
embodiments, first edge 1954a could either be parallel or
non-parallel to second edge 1954b. In various implementations, one
of the edges (e.g. edge 1955) of the neck patch 1906 can be used as
a right or left side angle indicator. The outer layer, e.g.,
non-adhesive portion of the neck patch 1906 can allow the vibratory
motor 1906 to freely move to stimulate the desired anatomical
structure(s).
[0190] FIG. 19A illustrates the outer-facing surface of an
embodiment of the neck patch 1906 which includes a first, e.g.,
base layer 1951 having a first outwardly facing surface and a
second inwardly facing (e.g., toward the patient) surface. The base
layer 1951 can comprise an attachment component on a surface, such
as the inwardly-facing surface. The attachment component could be,
for example, one-sided adhesive foam to attach the neck patch 1906
to the patient's skin. The base layer 1951 can include one, two, or
more openings, such as a central opening 1950 through which the
vibratory motor 1960 can be inserted and secured such that
therapeutic vibrational energy can be effectively transferred to
the required anatomical region. Opening 1950 can be square,
rectangular, or another shape to accommodate the particular
configuration of the sensory stimulation element. Sides of opening
1950 can be, in some embodiments, parallel or substantially
parallel to, or angularly offset to that of the peripheral edges of
the neck patch 1906. The peripheral edges of the neck patch could
be right angles, rounded as illustrated, or another shape. FIG. 19B
illustrates the inwardly-facing surface of the embodiment depicted
in FIG. 19A which can include an attachment component on a surface,
such as an adhesive portion 1953 on the patient-facing surface, a
non-adhesive portion 1956, and opening 1950. The non-adhesive
portion 1956 can include a clip retention element, a convolusion,
or an overhang. The adhesive portion 1953 can include single or
double-sided adhesive foam, for example, or another suitable
attachment, and in some cases is made of a hypoallergenic or
non-allergenic material. The adhesive portion 1953 can be used to
secure the neck patch 1906 to the patient's skin. FIG. 19C
illustrates the vibratory motor 1960 partially inserted through the
opening 1950 of the base layer of the neck patch 1906. The
vibratory motor 1960 can have one, two, or more force transfer
regions 1962 through which therapeutic energy is delivered to the
patient. In some embodiments, the force transfer regions 1962 can
be regularly or irregularly spaced projections as illustrated, that
could be arcuate, square, rectangular, triangular, or another
desired shape. In various implementations, the vibratory motor 1960
has a radially-extending base flange such that the base flange can
be secured between layers 1957, 1951 as shown in FIG. 19D. FIGS.
19E-19G illustrate embodiments of the retention layer 1957. The
retention layer 1957 can comprise one or more layers made of cloth,
foam, plastic, or some other material. In various implementations,
the retention layer 1957 can be made of a material that is similar
to or the same as other portions of the neck patch 1906, or of
different materials. The retention layer 1957 includes an opening
1959. The opening 1959 is adapted to fit over a portion of the
vibratory motor 1960 such that the motor 1960 is securely held. In
various implementations, the size and the position of the opening
1959 in the retention layer 1957 can be substantially similar to
the opening 1950 in the neck patch 1906. In other implementations,
the opening 1959 in the retention layer 1957 can be different in
size and position from the opening 1950 in the neck patch 1906. One
side of the retention layer 1957 has an adhesive portion 1958a
which adheres to the outer-facing surface of base layer 1951 of the
neck patch 1906 when the retention layer 1957 is secured to the
neck patch 1906. In some implementations, the outer-facing surface
1958b of the retention layer 1957 can be non-adhesive. The
retention layer 1957 can have alignment edges similar to the
alignment edges 1954a and 1954b that allow the patient to align the
neck patch 1906 including the retention layer 1957 to the
appropriate anatomical region for optimum therapeutic or comfort
placement.
[0191] FIG. 19H-19J illustrate various embodiments of a neck patch
1906 including a horseshoe-shaped clip 1952 using which the
vibratory motor 1960 can be more securely attached to the neck
patch 1906. The clip 1952 can include, for example a semi-rigid
material for secure attachment to the vibratory motor 1960. Other
attachments mechanisms can also be utilized, some of which have
been previously described.
III. Apnea Diagnosis Device
[0192] In various embodiments, a wireless home sleep monitor
including a radar-based physiological motion sensor can be used as
a sleep apnea diagnosis device as an alternative or in addition to
other sleep apnea diagnosis devices. The home sleep monitor system
can include a sensor and/or monitor configured to detect the apneic
events. In one embodiments, the system can be configured to detect
a period of apnea, paradoxical breathing, or other parameter that
occurs for about or at least about 5 seconds, 7 seconds, 10
seconds, 12 seconds, 15 seconds, 20 seconds, 30 seconds, 45
seconds, 60 seconds, or more in duration.
[0193] Apnea affects a large percentage of the population (a
majority of which do not know that they have apnea), and it would
be desirable to diagnose sleep apnea in the comforts of a patient's
home rather than performing a polysomnography study (PSG) which may
require an overnight stay at a hospital, sleep study, or other
healthcare facility. In various embodiments, a wireless home sleep
apnea diagnosis device can provide a more comfortable and/or
attractive alternative to those currently on the market which can
require bulky, uncomfortable, and/or noisy equipment. This wireless
monitor can combine radar-based, non-contact measurement of
respiratory effort and may contain other components, such as pulse
oximeter(s), nasal or oral airflow sensor(s), acoustic stethoscopes
or microphones, and/or chest and abdomen sensors with wired or
wireless communications, operating with or without wires on the
patient and with or without minimal contact to the patient. FIG.
17A describes a system with a wireless radar sensor 1902, airflow
sensor 1904, microphone sensor 1942, pulse ox sensor 1910, all
attached wired or wirelessly to the sensor processing unit 1908
with optional viewing through a tablet PC, PC, or mobile phone
1912. In various embodiments, the pulse oximeter, nasal or oral
airflow sensor(s), acoustic stethoscopes or microphones, and/or
chest and abdomen sensors can be configured to independently send
their data wired or wirelessly to the hub.
[0194] In various embodiments, it is possible to measure
respiratory motion without contacting the subject using a
radar-based system specifically configured to measure physiological
motion. Respiratory motion can be derived from the physiological
motion signal. In addition to detecting respiratory rates from the
motion, respiratory motion can provide a measure of respiratory
effort, similar to that provided by chest belts designed to measure
respiratory effort. Measurements of respiratory effort can be
useful in determining whether an event is a central apneic or an
obstructive apneic event. Respiratory motion can be measured with a
radar-based system overnight with the subject in any position in
the bed.
[0195] In various embodiments, the radar-based device or chest and
abdomen sensors can be configured to detect paradoxical breathing
when the abdomen contracts as the rib cage expands and/or when the
rib cage contracts as the abdomen expands. Typically, there is
paradoxical breathing during obstructive apnea, but paradoxical
breathing does not necessarily indicate an airway obstruction. An
indication of paradoxical breathing and/or of the level of
paradoxical breathing can be useful in detecting obstructive
apnea.
[0196] In various embodiments, the radar-based device can also
measure motion that is not due to respiration, which can indicate
activity such as tossing and turning in bed, wakefulness,
involuntary movement during sleep, the like, or any combination
thereof. The quality of sleep can be estimated based on level of
activity, and the level of activity can be helpful in determining
the sleep state of the subject. The radar-based device can also be
used to determine when the person is in the bed or out of the bed
and/or to track how often the subject is getting out of bed during
the night.
[0197] In some embodiments, the radar-based device may be
configured to generate data related to a number of physiological
parameters. For example, the radar-based device can generate data
used to measure and/or generate alarms. In various embodiments, the
radar-based device may also measure the heart rate. During apneic
events, the heart rate can increase substantially and/or an atrial
or ventricular arrhythmia could occur, thus the heart rate and/or
rhythm can be used to confirm an apnea that is indicated by other
measurements. For example, the device could detect a measured heart
rate increase over baseline of at least 10, 15, 20, 25, 30, 35, 40,
or more beats per minute or an absolute heart rate of 100, 110,
120, 130, 140, or more This can provide a higher confidence level
that an apneic event has been detected. In various embodiments, the
radar-based device can generate and/or display an indicator of a
confidence level of detecting an apneic event.
[0198] In various embodiments, the radar-based device may be used
to estimate the tidal volume, or the amount of air inhaled and
exhaled with each breath. When the tidal volume is accurately
measured, the tidal volume can be used to estimate the airflow.
[0199] In various embodiments, the radar-based device may include
multiple-antenna hardware and software such that it can track the
subject as he/she moves in bed during the night. This can provide
information about how much the subject is moving within the bed and
can improve the radar-based measurement of respiration and
activity.
[0200] In various embodiments, the radar-based device may be used
in conjunction with one or more other sensors to provide a more
complete picture of respiration and apneic events during sleep.
Additional sensors may include but are not limited to the pulse
oximeter, blood pressure measurement device, nasal or oral airflow
sensor(s), acoustic stethoscopes or microphones, and/or chest and
abdomen sensors
[0201] In various embodiments, the nasal/oral airflow sensor,
acoustic stethoscope and/or microphone can provide an indication of
whether the patient is breathing and/or, with a more advanced
sensor, an estimate of the velocity of the airflow. This can be
used to accurately detect apnea, and with the more advanced
sensors, also detect hypopnea (reduction in airflow). An accurate
measurement of airflow can be useful in determining whether an
event is a hypopneic or an apneic event. In some embodiments,
reduction in airflow can be determined by a detection of a decrease
in airflow of at least about 10%, 20%, 30%, 40%, 50%, or more over
baseline. The nasal/oral airflow sensor may include one or more
thermistors, hot-wire anemometers, pressure sensors, the like, or
any combination thereof. For example, there may be more than one
sensor when the airflow in each nostril and/or at the mouth are
measured independently.
[0202] In various embodiments, the respiratory monitoring device,
e.g., pulse oximeter can provide information on the effectiveness
of respiration by arterial hemoglobin saturation, an estimate of
blood oxygenation. Decreases in blood oxygenation can indicate the
severity of an apneic and/or hypopneic event, and can be clinically
significant. The pulse oximeter can also provide a heart rate
measurement. Pulse oximetry data can be obtained from sensors on
the finger or on the ear.
[0203] In various embodiments, the pulse oximeter, nasal or oral
airflow sensor(s), acoustic stethoscopes or microphones, and/or
chest and abdomen sensors contact the patient, but in accordance
with a number of embodiments described herein the pulse oximeter,
nasal or oral airflow sensor(s), acoustic stethoscopes or
microphones, and/or chest and abdomen sensors can advantageously
transmit data wired or wirelessly to the data recording device.
This recording device may be integrated with the radar-based
device.
[0204] In various embodiments, this wireless home sleep monitor,
including the radar-based device, pulse oximeter, nasal or oral
airflow sensor(s), acoustic stethoscopes or microphones, and/or
chest and abdomen sensors operating wired or wirelessly and with
minimal contact to the patient, can provide a detailed picture of
respiration during sleep including measurements related to:
airflow, respiratory effort, and oxygenation. It can also provide
measurements related to one or more of the following: the heart
rate and/or rhythm, variability in the heart rate, and information
about motion during sleep. The pulse oximeter, nasal or oral
airflow sensor(s), acoustic stethoscopes or microphones, and/or
chest and abdomen sensors can independently send their data wired
or wirelessly to the hub, such that few or no wires would be
required. This can provide a significant advantages over other
commercially available home sleep monitors, which require wires to
the recording device or wires to a single body-worn device with
then wirelessly transmits data to the recording device.
[0205] In various embodiments, the system can include one or more
of a non-contact radar sensor 1902 aimed at the chest to detect
ventilatory effort, a microphone embedded 1932 in a neck patch
sensor 1906 to monitor airflow, a nasal airflow sensor 1904 as an
auxiliary airflow monitor, a pulse oximeter sensor 1910 to detect
oxygen saturation and heart rate, and/or an accelerometer to detect
body motion. One or more of the sensors can be coupled with a
sensor processing unit 1908 worn on the patient's arm or other
location that may process detecting apneic events. One or more of
the sensors may be wired to the sensor processing unit or may
wirelessly communicate to the sensor processing unit. The system
may include, e.g., a web based or PC based application software
1940 to assist the clinician in assessing subject's apnea severity
by reporting sleep breathing disorder events and computing and
reporting the AHI, event duration, and timestamps.
[0206] In various embodiments, the system can include one or more
of an acoustic stethoscope or airflow sensor able to detect
respiration, airflow and/or respiration rate, contact chest and
abdomen sensor or sensors able to detect ventilatory effort,
paradoxical breathing, and/or respiration rate, and/or strain gauge
or other sensing technology such as PVDF to detect movement in
response to stimulation.
[0207] In various embodiments, the thresholds to detect an apnea
event on the sensor processing unit may be set by the manufacturer,
hospital, healthcare practitioner, or subject.
[0208] In various embodiments, the sensor may be coupled with a
smartphone or computer tablet which may include its own display,
user interface and controls, clock, recording hardware and
software, and/or communications hardware and software.
[0209] In various embodiments, the device may include an embedded
processor to process the signals and control the inter-sensor
communications to relay data to the stand alone devices, such as a
sensor, smartphone, or computer tablet.
[0210] In various embodiments, the device may include, e.g., a web
based or PC based application software 1940 to assist the clinician
in assessing subject's apnea severity by reporting sleep breathing
disorder events and computing and reporting the AHI, event
duration, and timestamps, and/or other patient information.
[0211] In various embodiments, the sensor processing unit 1908 may
be housed in an enclosure worn on the arm or other location. The
sensor processing unit 1908 may include the CPU 1918, memory 1920
to store the respiratory waveforms and events, power management
circuitry, rechargeable battery 1924, pulse oximetry processor
1926, a transceiver 1922 to communicate with and collect data from
external sensors, and/or a wired connector and cabling 1938 to
collect sensor data, as shown in FIG. 17B.
IV. Snore Therapy Device
[0212] In various embodiments, a system for detecting and treating
snoring is provided. The system can include one or more microphones
or other transducer which may utilize a snoring detection algorithm
to determine the onset of a snoring event. The therapeutic device
can be configured to perform at least one action related to
detecting and treating a snoring event, which could in some cases
signal partial or complete airway obstruction, and/or affect the
sleep of others within hearing range of the patient. As shown in
FIG. 18A, the therapeutic device 2002 may comprise a bio-feedback
mechanism configured to stimulate, for example, an anatomical or
physiologic sector associated with airway obstruction, including
the hypoglossal nerve region in the patient's neck when snoring
event is detected causing the patient to shift position, swallow,
cough, move the palate or tongue, or restore muscle tone in the
genioglossus muscle in the patient's neck, thereby restoring the
patency of the upper airway passage and possibly terminating or
alleviating the snoring event. The therapeutic device may include
electrical, electromechanical, or purely mechanical devices,
including but not limited to a vibrating transducer and/or
electrodes which can produce electrical signals to produce
mechanical stimulation including vibrations to stimulate the
subject's neck muscles which will increase in sensation until the
snoring event is stopped. The device may be coupled with a separate
stand-alone device, such as a sensor, smartphone, or computer
tablet 2004 with its own display, user interface and controls,
clock, recording hardware and software, and/or communications
hardware and software.
[0213] In various embodiments, the snore therapy device can be
configured to detect a period of snoring, or other parameter that
occurs for about or at least about 1 second through 15 seconds,
inclusive, or more in duration. One goal is to terminate the
snoring event without affecting the subject's sleep architecture or
without arousing the subject from sleep at which point the snore
therapy device will stop stimulation and return to its idle or
normal state until the next snoring event.
[0214] Snoring affects a large percentage of the population, and it
would be desirable to treat snoring without surgery or cumbersome
devices attached to the subject's body. In various embodiments, a
contact therapeutic device can provide a more comfortable and/or
attractive alternative to those currently on the market (e.g.,
surgery, oral appliance, etc.). Removable devices currently on the
market cause discomfort to the subject and eventual lack of use by
the subject, and surgery presents a risk due to the implant system.
Thus, there is a need for improved treatment to snoring that can
address the discomfort to the existing approaches. This snore
therapy device may contain components, such acoustic stethoscopes
or microphones, operating with or without wires on the patient and
with or without minimal contact to the patient
[0215] In various embodiments, the acoustic stethoscope and/or
microphone(s), or other devices can provide an indication of
whether the patient is snoring. This can be used to accurately
detect snoring.
[0216] In various embodiments, the acoustic stethoscopes or
microphones can advantageously transmit data wired or wirelessly to
the data recording device.
[0217] In various embodiments, the thresholds to detect a snoring
event may be set by manufacturer, hospital, healthcare
practitioner, or subject.
[0218] In various embodiments, the neck patch 2002 may be
capacitively coupled to automatically power on when placed in
use.
[0219] The device may comprise a strain gauge or other sensing
technology such as PVDF to detect movement in response to
stimulation.
[0220] In various embodiments, the neck patch 2002 may include a
rechargeable or replaceable battery 2024 which may include a
blinking light or annunciation of the battery condition.
[0221] In various embodiments, the neck patch 2002 may include
storage of data or a web interface.
[0222] In various embodiments, the neck patch 2002 may be coupled
with a separate stand-alone device, such as a sensor, smartphone or
computer tablet which may include its own display, user interface
and controls, clock, recording hardware and software, and/or
communications hardware and software.
[0223] In various embodiments, the device may include an embedded
processor to process the signals and control the inter-sensor
communications to relay data to the stand alone devices, such as a
sensor, smartphone, or computer tablet.
[0224] In various embodiments, the neck patch may be in disposable
form.
[0225] In various embodiments, the device may include a web based
or PC based application software to assist the subject in assessing
subject's snoring severity by reporting snoring events and
computing and reporting the number of events, event duration and
timestamps.
[0226] In various embodiments, the neck patch 2002 is constructed
from biocompatible materials, including a replaceable substrate
with a biocompatible adhesive. The substrate has an opening for a
semi-rigid vibration plate attached to the motor, permitting direct
contact of the vibration plate to the skin. The substrate has an
additional opening permitting the microphone to be in close
proximity to the skin near the larynx. The substrate has
indentations to accommodate the neck patch sensor processing unit
2026, battery 2024, microphone 2022, and motor 2018, as shown in
FIG. 18B. The neck patch cover encloses the components and
substrate.
[0227] In various embodiments, PC, smart phone, or tablet PC 2004
may include one or more of following: the application software
2006, CPU 2008, memory 2010 to store the snoring events, microphone
2012, rechargeable battery 2016, a transceiver 2014 to communicate
with and collect data from external sensors, and/or a wired
connector and cabling to collect sensor data 2028, as shown in FIG.
18B.
V. Sway Cancellation
[0228] One potentially significant source of interference in
measuring the respiration and/or heart signals of a human subject
while standing can be the back and forth sway of the standing
subject. A system including two, three, four, five, or more sensors
can be used to detect and/or cancel out sway motion. The two or
more sensors can be positioned in any suitable location to detect
motion of a subject, such as a patient. The two or more sensors can
include two or more radar sensors. For example, the system can
include a first sensor configured to detect sway motion of a
patient at a first location, and a second sensor spaced apart from
the first sensor configured to detect sway motion of a patient at a
second location. The first sensor and the second sensor can be
positioned, for example, at opposing sides of the subject. More
specifically, in some embodiments, the system can include a first
sensor at the front of a subject and a second sensor at the back of
the subject. Alternatively or additionally, in some embodiments,
the system can include a first sensor at a right side of a subject
and a second sensor at a left side the subject. While sensors can
be oriented about 180 degrees apart with respect to the subject, an
angle between the first sensor, the subject/patient, and second
sensor can be between about 160 and 220 degrees, between about 150
and 210 degrees, between about 100 and 260 degrees, or other angles
for example depending on the desired clinical result.
[0229] Although some features are described with reference to a
system with a first sensor at the front of the patient and a second
sensor at the back of the patient for illustrative purposes, any
combination of the principles and advantages of the system can be
applied to any other system with two or more sensors configured to
generate sway data related to two or more locations of a patient,
for example, as described above. In some embodiments, a system with
two or more radar sensors can be used to detect a subject's motion
from both the front and back at substantially the same time. When
the subject is swaying, the motion signals from the radar sensors
can represent a combination of swaying and respiration motion. A
subject's swaying motion can generate a signal in the back sensor
with the opposite polarity of the signal generated in the front
sensor. However, since cardiopulmonary motion can cause expansion
and contraction of the subject's torso, such that all sides move
towards the body's center or away from the body's center,
cardiopulmonary motion can generate signals with the same polarity
that will be the same in the front and back sensors. In some
embodiments, the signals from the front and back sensors may be
added to minimize the swaying motion, while approximately doubling
the amplitude of the cardiopulmonary signal. In some embodiments,
an additional benefit of this method is an increased
signal-to-noise ratio (SNR), indicating a stronger signal relative
to noise, because the summation of two independent outputs can
reduce white Gaussian noise, thus resulting in higher SNR.
[0230] In some embodiments, a linear combination of the signals
from the two sensors can be calculated to cancel the swaying
motion, when the amplitude of the two signals is not equal. In some
embodiments, this linear combination may be calculated using an
adaptive filter. In some embodiments, the adaptive filter may be
based on a least mean squares algorithm. In some embodiments, an
additional sensor signal that detects sway but not respiration,
such as a laser sensor fixed on a part of the body that sways but
does not move with respiration, or a signal from a load cell may be
used as a reference input for the adaptive filter. In some
embodiments, the linear combination of the two radar signals may be
calculated to minimize the signal power, in which the weighting
factors for each signal can be positive such that the respiratory
signal is not likely to be cancelled. In some embodiments, the
linear combination may be calculated using demodulated signals. In
some embodiments, a linear combination of the quadrature signals
may be calculated before demodulation. In some embodiments, the
signals may be rotated in the I/Q plane and the radii adjusted such
that the lines or arcs on which they are projected are co-located
and then a least mean square adaptive filter may be applied to the
quadrature representation of the signals: Q+jI.
[0231] In some embodiments, the powers of the first and second
signal can be different as the radar cross section of a subject's
front and back may vary. The signals from the front and back sensor
may also be affected by different delays. In some embodiments, a
complex weight factor may be used to compensate these effects, as
represented by the following expression:
[0232] Ae.sup.j.theta., where A can represent power and .theta. can
represent phase.
[0233] In some embodiments, the complex weight factor can be
selected by solving for A and 0 to minimize undesired signal power
for the sum of the front and back signal. In some embodiments, the
undesired signal power may be that of a certain bandwidth. In some
embodiments, the undesired signal power may be some specific
frequency such as that of the swaying motion. In some embodiments,
MMSE estimation may be used to solve for A and .theta.. In some
embodiments, LSE may be used to solve for A and .theta..
[0234] In some embodiments, the sway signal may be isolated from
the respiratory signal using independent components analysis, or
blind source separation algorithms applied to the signals from the
two sensors. In some embodiments, empirical mode decomposition
algorithms may be applied to the signals from the two sensors to
separate the respiratory signal from the swaying signal. In some
embodiments, after an algorithm is applied to isolate the two
signals, the respiratory signal can be determined using an
algorithm that uses signal features to identify whether or not a
signal corresponds to respiration. In some embodiments, after an
algorithm is applied to isolate the two signals, the swaying signal
can be determined as the one that most closely matches the signal
from another sensor used to detect swaying without detecting
respiratory motion.
[0235] In some embodiments, a third sensor can be used to help
identify if a subject is swaying or not swaying, or otherwise
moving. In some embodiments, the sensor may be a load cell. It can
be desirable for the load cell to have enough resolution to
determine a weight shift of the subject as he or she sways forward
and backward. Such a sensor may also identify whether a swaying
motion is periodic and, if so, what determine a frequency of
motion. In some embodiments, if no swaying is detected, the signal
from the front sensor can be used to obtain a subject's
cardiopulmonary motion. In some embodiments, the back sensor may be
used. In some embodiments, both signals may be considered and the
stronger signal is used. In some embodiments, both signals may be
considered and the signal having lower noise and/or interference
may be used. In some embodiments, both signals may be considered
and the signal with less non-cardiopulmonary motion identified can
be used. In some embodiments, both sensors can function as a
diversity system; thus simple summation of the sensors outputs can
be used to obtain a subject's cardiopulmonary motion with higher
SNR. In some embodiments, each sensor may demodulate incoming
signals independently followed by adaptive filtering to maximize
cardiopulmonary motion signals.
[0236] In some embodiments, the third sensor used to identify
subject swaying can be an optical sensor such as that based on a
laser. In some embodiments, this optical sensor can be focused on
an area of the body that may sway without having significant
respiratory motion, such as the legs and/or the head. In some
embodiments, the third sensor used to identify subject swaying can
be an ultrasound sensor.
[0237] In some embodiments, a dual sensor approach with sensors 1
and 2 placed in front of and behind the standing subject can be
used to record and cancel the sway movement and recover the
physiological signals, with sample results shown in FIG. 15A.
[0238] In some embodiments, as shown in FIG. 15B, one or more of
the following operations can be performed on the signals obtained
from both of the sensors: [0239] 1. Acquire time synced I and Q
signals from both of the sensors. x.sub.t1, x.sub.q1 from sensor 1
and x.sub.i2, x.sub.q2 from sensor 2. [0240] 2. Perform Principal
Component Analysis (PCA) on x.sub.t1, x.sub.q1 and call the result
D1 [0241] 3. Perform Principal Component Analysis (PCA) on
x.sub.t1, x.sub.q2 and call the result D2 [0242] 4. Perform PCA on
D1 and D2. Choose the output with the smaller eigen value as a
physiological signal and the output with larger eigen value as the
sway component.
V. Detection of Apneic Events and Cessation of Breath
[0243] The subset of frames can include samples obtained over a
period of time longer than the expected period of respiration. In
some embodiments, the subset of frames can include samples obtained
over a period of time longer than the expected cycle period of
irregular respiration. The method can also include using a wavelet
transform function to create an index of repeating patterns in a
respiration signal. In some embodiments, the irregularity of the
breath-to-breath interval, or breath duration, can be estimated
from one or more of the following: the standard deviation of the
breath-to-breath interval, the frequency of apneaic events, the
coefficient of variation of the breath-to-breath interval, the
standard deviation of the respiratory rate, and the coefficient of
variation of the respiratory rate. In some embodiments, the
irregularity of the amplitude of a breath and/or the depth of
breath, and/or breath duration, can be estimated from the standard
deviation of the breath depth, the coefficient of variation of the
breath depth, the standard deviation of the respiratory signal
amplitude, the coefficient of variation of the respiratory signal
amplitude, or any combination thereof. Information regarding the
irregularity or regularity of respiration can include assessment of
whether irregular breathing is periodic. This assessment can
include estimating each breath-to-breath interval, and storing it
with the time point at the end of the interval in which it was
calculated; interpolating between these breath-to-breath intervals
to create a waveform; performing the Fourier transform, performing
the autocorrelation function, and/or calculating the power spectral
density of the waveform; determining whether there are significant
peaks of the Fourier transform, the autocorrelation function,
and/or the power spectral density of the waveform; and determining
that if significant peaks exist, the breathing is irregular and
periodic, or any combination thereof. The assessment can also
include interpolating between these breath-to-breath intervals to
create a waveform; identifying peaks of the waveform; determining
the time between the peaks; calculating the coefficient of
variation of the time between the peaks; determining if the
coefficient of variation of the time between the peaks is low, the
breathing is irregular and periodic; and determining if the
coefficient of variation of the time between the peaks is low, the
breathing is irregular and is not periodic, or any combination
thereof. In some embodiments, the assessment of whether irregular
breathing is periodic comprises one or more of the following:
identifying apneaic events; determining the time of cessation of
apneaic events; estimating the interval between the cessation of
each consecutive pair of apneaic events; determining whether the
interval between the cessation of each consecutive pair of apneaic
events is consistent by calculating the coefficient of variation of
the interval between the events by calculating the coefficient of
variation; determining if the coefficient of variation is below a
threshold, breathing is periodic; and determining if the
coefficient of variation is above a threshold, breathing is
irregular and not periodic. In some embodiments, assessment of
whether irregular breathing is periodic comprises one or more of
the following: calculating the envelope of the respiratory
waveform; performing the Fourier transform, performing the
autocorrelation function, or calculating the power spectral density
of the waveform; and determining whether there are significant
peaks of the Fourier transform, the autocorrelation function, or
the power spectral density of the waveform. In some embodiments,
the envelope is calculated by interpolating between the peak
amplitudes, or squaring the signal and applying a low-pass
filter.
[0244] In some embodiments, features that highlight the core
aspects of a breathing signal can be extracted from a database of
breaths. In some embodiments, these features can include the inhale
time to exhale time ratio, the length of pauses in breathing, the
ratio of the length of a pause in breathing to the breathing
period, the depth of breath, the inflection points of the breath,
and/or the mean, variance and kurtosis of the breath, or any
combination thereof. In some embodiments, these features can
include particular coefficients in the wavelet decomposition of the
signal or particular coefficients of the Fourier transform of the
signal. In various embodiments, the same features extracted from
the database of breathing signals can be again extracted from the
new signal being considered. In some embodiments, the new signal
features can be compared to the database of features, and if a
match is found, then the signal can be labeled as a breath. In some
embodiments, the peak of the breath can be identified based on
information in the database.
[0245] Various embodiments of the respiratory regularity assessment
algorithm can determine whether irregular breathing is periodic. In
various embodiments, one or more of the following methods can be
used to determine whether irregular breathing is periodic: [0246]
Interpolate between the breath-breath interval calculations (with
the data set encompassing the length of the interval vs. time, with
the time point at the end of the breath for which the interval in
which it was calculated) and perform the Fourier transform and/or
calculate the power spectral density of the resulting waveform.
Determine if it has a significant periodic component. [0247]
Interpolate between the breath-breath interval calculations (with
the data set encompassing the length of the interval vs. time, with
the time point at the end of the breath for which the interval in
which it was calculated) and perform an autocorrelation. Determine
if it has a significant periodic component. [0248] Interpolate
between the breath-breath interval calculations (with the data set
encompassing the length of the interval vs. time, with the time
point at the end of the breath for which the interval in which it
was calculated) and determine peaks of the resulting waveform.
Determine if the difference between the peaks is consistent by
calculating the coefficient of variation of the difference between
the peaks and determining whether it is low enough to indicate
periodic breathing. [0249] Identify the cessation of apneaic
events, and determine the cessation-of-apnea to cessation-of-apnea
intervals. Determine whether the difference between the cessation
of apneas is consistent by calculating the coefficient of variation
of the difference between the events and determining whether it is
low enough to indicate periodic breathing by comparing to a
threshold. [0250] Identify the cessation of apneaic events, and
determine the cessation-of-apnea to cessation-of-apnea intervals.
Calculate the average time difference between the cessation of
apneas as the cycle length of periodic breathing.
[0251] FIG. 8A illustrates a flow chart of a method that is used to
assess the regularity of respiration. The method can comprise one
or more of the following operations: [0252] 1. Estimate the
breath-to-breath interval and the depth of breath for each breath
as respiration is processed as shown in block 1040. [0253] 2. Over
an interval of 50 breaths, calculate the mean and standard
deviation of the breath-breath interval, and the mean and standard
deviation of the depth of breath as shown in block 1042. [0254] 3.
Calculate the coefficient of variation of the breath-to-breath
interval and the depth of breath as shown in block 1044. If neither
one is above a threshold, the respiration can be considered regular
as shown in block 1046. If the coefficient of variation of either
the breath-breath interval or the depth of breath is above a
threshold, the respiration can be considered irregular as shown in
block 1048, and additional processing is performed. In some
embodiments, the threshold can be 25%. [0255] 4. If the respiration
is detected as irregular, determine whether the cycle time is
periodic by interpolating between breath-breath intervals and depth
of breath estimates, taking a Fourier transform of each waveform,
and determining whether a periodic component exists in either
waveform as shown in block 1048. If a periodic component exists in
at least one of the waveforms, the cycle time can be indicated as
periodic as shown in block 1052. If a periodic component does not
exist in either waveform, the cycle time is not indicated as
periodic as shown in block 1054. [0256] 5. If the cycle time is not
indicated as periodic, repeat operation 2 with a longer interval of
breaths (150 breaths). If the cycle time is still not indicated as
periodic, skip to operation 7. [0257] 6. If the cycle time is
indicated as periodic, calculate the cycle time finding by peaks in
the interpolated breath-breath interval in operation 4 and
determining the mean time between the peaks as shown in block 1052.
If multiple peaks are not available, extend the interval used for
this operation. [0258] 7. If the cycle is not indicated as
periodic, isolate the breath-breath intervals longer than 20
seconds as shown in block 1056. Calculate the number of these
intervals divided by the total time interval used for calculation.
Calculate the mean of these apneaic events. [0259] 8. If the cycle
is indicated as periodic, determine the length of apnea in each
period, and average this number to get the average apnea length per
cycle as shown in block 1058. [0260] 9. Display the data as shown
in block 1060. If respiration is detected as regular, indicate that
respiration is "regular". If respiration is detected as irregular,
indicate either "periodic--cycle time X" where X is the cycle time
or "irregular." If apneaic events exist, indicate "--average apnea
length Y" and, if respiration is not periodic also indicate "--Z
apneaic events/minute."
[0261] In some embodiments, the following algorithm can be used to
provide indication of irregularity. Rates calculated by the rate
estimator 1074 can be stored in a FIFO buffer 1070 of length N,
where N is an integer. N can represent the amount of data used to
calculate the irregular breathing index. The sum of the absolute
value of the differences of the rate values stored in the FIFO
buffer 1070 can then be taken, as shown in FIG. 8B. For elements 1
to N of buffer x, the block DIFF 1072 will return [x2-x1 x3-x2 . .
. xn-xn-1]. The output of this calculation can be the irregular
breathing index. This index can then be compared with a
predetermined threshold such that if the irregular breathing index
is greater than the threshold, a subject's respiratory pattern can
be considered irregular.
[0262] Obstructive apnea can be defined as an 80-100% reduction in
airflow signal amplitude for a minimum of 10 seconds with continued
respiratory effort. The rib cage and abdomen can move out of phase
as the patient tries to breathe, but the airway can be blocked. A
quadrature Doppler radar system, such as the one described above,
can monitor this paradoxical breathing based on the complex
constellation due to the target's chest and abdomen motion. Since a
human's physiological signal such as breathing is a very narrow
band signal (.about.less than 1 KHz) compared to the radar carrier
signal, all the reflected signals will be phase modulated on a
coherent carrier signal. Therefore, if human body parts, for
example the chest and abdomen, are expanding or contracting
simultaneously, the received reflecting signals from different
paths (reflecting from different body parts) may only shift the
phasor of the carrier signal but not the phase modulated narrow
band carrier signals. Shift of the phasor of phase modulated narrow
band carrier signals can also occur when different body parts are
moving at the same frequency but with different amplitude or phase
delay, as is the case in paradoxical breathing. Consequently, in
the former case, the shape of the complex plot at the baseband due
to the respiration may not change and can form a fraction of a
circle (an arc) which can be similar to the one from the a single
source, while in the latter case the phasor of the baseband signal
changes during the periodic motion (such as breathing), resulting
in distortion of the complex constellation. This fact can be used
to detect paradoxical breathing.
[0263] The paradoxical factor can be calculated as the ratio of the
largest eigenvalue to the second largest eigenvalue multiplied by
the ratio of the maximum amplitude of the signal projected on the
principal vector to the maximum amplitude of the signal projected
onto the vector orthogonal to the principal eigenvector. A cost
function can convert the paradoxical factor to a paradox indicator,
which can be used to indicate paradoxical breathing.
[0264] The input to the cost function can be the paradoxical factor
and the cost function can transform the paradoxical factor to a
value which is between 0 and 1. In some embodiments, the cost
function can be represented by the following equation
Cost ( input ) = 1 v .times. 2 .times. .pi. .times. .intg. x
.times. 1 x .times. 2 exp ( - ( input - m ) 2 2 .times. v 2 )
.times. dx , ##EQU00006##
where x1, x2 can represent a range of the paradoxical factor, which
can be 0 and 1, while m and v can represent boundary input values
between paradoxical and non-paradoxical and v can represent
emphasizing factor of paradoxical factor. For example, if m is
close to x1 then paradoxical indicator threshold can be set to
lower paradoxical factor. On the other hand, as v increases
paradoxical indicator can changes more dramatically as paradoxical
factor changes. If the paradoxical indicator is near one, it can be
likely that there is paradoxical breathing; if the paradoxical
indicator is near zero, it can be unlikely that there is
paradoxical breathing. A threshold can be set on the paradoxical
indicator to provide a yes/no output, or two thresholds can be
applied to achieve a green-yellow-red output corresponding to
likely paradoxical breathing, uncertain output, and unlikely
paradoxical breathing.
[0265] In one embodiment, m can be set to approximately 0.3 and v
can be set to approximately 0.04.
[0266] In some embodiments, the realization of respiration
cessation monitor can be based on estimating the relative amplitude
of the breathing waveform during the times of no motion artifact.
The amplitude samples can be used to create a histogram which can
then be used to determine the threshold for cessation of
breath.
[0267] In some embodiments, the method for realization can include
one or more of the following: [0268] 1. Determine the time spans of
no motion (fidgeting or activity). On the time spans that are more
than L1 seconds, perform the following, with sample results shown
in FIG. 16: [0269] a. Calculate the instantaneous amplitude
(envelope) vs. time of the breathing signal by squaring the signal
and passing it through a moving average filter of length L2
seconds. [0270] b. Generate the cumulative histogram of the
amplitude obtained in a. [0271] c. Set the thresholds for low
breathing amplitude based on the cumulative histogram. [0272] d.
Within the `no motion` time span, find apnea timespans as those
when the instantaneous amplitude drops below the threshold. [0273]
2. Report the timestamps of the apneic events obtained from (a)-(d)
and redo the operation for the next time span.
VI. Multi-Parameter Vital Signs Measurement Systems
[0274] In various embodiments, the nasal/oral airflow sensor can
provide either an indication of whether the patient is breathing,
and/or, with a more advanced sensor, an estimate of the velocity of
the airflow. A number of respiratory events, such as
non-respiration and/or reduced respiration events, can be detected
based on the data generated by such sensors. For example, this data
can be used to accurately detect apnea, and with the more advanced
sensors, it can also be used to detect hypopnea (reduction in
airflow). An accurate measurement of airflow can be useful in
determining whether an event is a hypopnea or an apnea. The
nasal/oral airflow sensor can include one or more thermistors,
hot-wire anemometers, pressure sensors, or any combination thereof.
In some embodiments, a nasal/oral airflow sensor can be provided to
measure the air flow through each nostril and the mouth
independently. In a number of embodiments, an airflow sensor alone
may encounter difficulties determining whether an apnea is central
or obstructive.
[0275] As shown in FIG. 4A, some embodiments of the system 100 can
include a sensor unit 604 that is wirelessly linked with a patient
monitor 605. The patient monitor 605 can be located in any suitable
location. For example, in some embodiments, the sensor unit 604 can
be located in relatively close proximity to the patient monitor
605, such as in the patient's room. The system 100 can be
configured to wirelessly transmit the digitized signals from the
sensor unit 604 to the patient monitor 605 in the patient's room
and/or in other locations. The patient monitor 605 can include a
processing unit 606 that can be configured to process the signals
from the sensor unit 604. The processing can include, but is not
limited to, DC compensation, filtering, demodulation,
motion-detection, rate-finding, possible calculation of other
variables, or any combination thereof.
[0276] As illustrated in FIG. 4B, in various embodiments, the
sensor unit 604 can include the processing unit 606 and associated
digital components such that the sensor unit 604 is configured to
process the digital signal, including perform DC compensation,
filtering, demodulation, and/or motion detection, and transmit a
processed signal to the patient monitor 605. In various
embodiments, the processing unit 606 in the sensor unit 604 can be
configured to perform rate estimation and/or calculation of other
respiratory variables, or, alternatively, the patient monitor 605
can perform rate estimation and/or calculation of other respiratory
variables from the processed signal. In those embodiments in which
the patient monitor 605 is configured to perform rate estimation,
the patient monitor 605 can use the same rate-estimation algorithm
used for other respiratory waveforms it can input, including, for
example, impedance pneumography.
[0277] FIG. 5 illustrates a flowchart of an embodiment of a method
for performing DC cancellation 800. At the beginning, an
analog-to-digital converter (ADC) can acquire the motion signal
obtained by transforming the Doppler shifted received signal as
shown in block 801. If in block 802, it is determined that the
signal is being clipped, then the method can proceed to block 803.
In block 803, the estimated DC offset can be adjusted depending on
at least one or more of the following factors: gain of the system,
input range of the ADC and various other factors as shown in blocks
803a and 803b. The estimated DC offset value can be output to a
digital-to-analog converter (DAC) as shown in block 803c. A good
signal buffer configured to store continuously acquired signal that
has no clipping or negligible clipping can be cleared as shown in
block 804, the method can return to block 801 and the signal is
re-acquired.
[0278] In various embodiments, a sensor network including a
plurality of "thin" cardio pulmonary sensors can work in
conjunction with a centralized processing appliance. FIG. 13
illustrates a centralized topology such that a plurality of "thin"
non-contact cardiopulmonary sensors form clusters 3901a and 3901b.
In some embodiments, the clusters 3901a and/or 3901b can include at
least 2, 3, 4, 5, 6, 7, 8, 9, 10, or even more sensors. The sensor
clusters can be controlled by a network appliance 3902 where all or
substantially all processing can take place. Embodiments of this
topology can be useful where sensors can be deployed in a
relatively dense area (for example, one per hospital bed). In this
case, rather than having each sensor be a full fledged cardio
pulmonary monitor, each sensor may only possess minimal hardware,
in some embodiments, only enough for data acquisition and
forwarding a data stream. In various embodiments, each sensor can
include a data acquisition module and a network module. Data can be
transferred from one or more devices in the clusters 3901a and/or
3901b via a network, such as a local network, intranet, the
Internet, or any combination thereof. In various embodiments, raw
data can be streamed to the network appliance 3902 where further
processing can be performed. In various embodiments described
above, the system can process the raw data internally. In various
embodiments, processing can include the demodulation of the IQ
channels, any DOA processing for tracking, respiration rate, etc.
In various embodiments, the calculated statistics and processed
data can then be stored on the network appliance 3902 and/or they
can be forwarded to an electronic health record server and/or other
non-transitory computer memory. A remote client can then access
this data via any suitable electronic device, such as a computer,
tablet computer, mobile phone, PDA, etc. The data can also be
viewed via a terminal locally and/or remotely in various
embodiments. FIG. 39B shows an alternate embodiment of FIG. 13
showing the direction of information flow between the sensor
cluster 3901a, the network appliance 3902 and various other
components of the network.
[0279] Patient monitoring devices can be used in medical settings
to monitor a patient's physiological waveforms, including, but not
limited to, electrocardiogram, respiratory effort, respiratory
airflow, pulse, blood oxygenation as well as vital signs, including
but not limited to heart rate, pulse rate, respiratory rate, blood
oxygenation, end-tidal CO2, or any combination thereof. Vital signs
measurement devices can be used in medical settings to measure a
patient's vital signs at a point in time and/or at regular
intervals, including, but not limited to heart rate, pulse rate,
respiratory rate, blood oxygen, temperature, end-tidal CO2, blood
pressure, or any combination thereof. Some embodiments are directed
to a Doppler radar-based device that provides a non-contact sensor
of physiological motion that is integrated into a patient
monitoring device and/or a vital signs measurement device. The
physiological motion signal obtained with the Doppler radar-based
device can be analyzed to provide one or more of: respiratory rate,
heart rate, other respiratory parameters, other heart parameters,
and physiological signatures, including, but not limited to,
respiratory pattern and heart pattern. These signatures may be used
to determine the physiological state of the subject, which may be
used for medical applications. The device can distinguish valid
measurement of heart and respiratory activity, and provide
continuous, point in time, intermittent and/or piecemeal data from
which rates, signatures, and key variations can be recognized. This
device can operate with no contact and can operate at a distance
from a subject. The device can operate on subjects in any position,
including lying down, reclined, sitting, or standing.
[0280] Non-contact physiological motion sensors, according to some
embodiments, may be used to obtain respiratory rate, heart rate,
and/or physiological waveforms that can be analyzed to help assess
the physiological state of the measurement subject. The
physiological information may be used for many applications,
including but not limited to various medical applications.
[0281] Embodiments of the device operate with no contact and work
at a distance from a subject. The device can operate on subjects
that are in any position, including lying down, reclined, sitting,
or standing. The device can operate at various distances from the
subject, from, for example, approximately 0.1 to 4.0 meters.
[0282] In some embodiments, the device can be positioned in various
locations relative to the subject, including but not limited to, in
front of the subject, behind the subject, above the subject, below
the subject, to the side of the subject, or at various angles to
the subject.
[0283] In some embodiments, physiological waveforms that may be
obtained include, but are not limited to, respiratory effort, chest
wall movement due to the underlying heart, peripheral pulse
movement, or any combination thereof. Information derived from
these waveforms may include, but is not limited to, one or more of
the following: [0284] Respiratory [0285] Rate [0286] Inhale time
[0287] Exhale time [0288] Inhale time to exhale time ratio [0289]
Frequency, depth, and length of gasps [0290] Frequency, depth, and
length of sighs [0291] Depth of breath [0292] Degree of paradoxical
breathing [0293] Tidal volume [0294] Abdominal excursion to chest
excursion ratio [0295] Harmonic content of breathing signal [0296]
Shape of the breathing waveform [0297] Heart and pulse [0298]
Average Rate [0299] Beat-to-beat interval [0300] Heart Rate
Variability [0301] Blood pressure [0302] Pulse transit time [0303]
Cardiac output [0304] Other [0305] Correlation between heart and
respiratory rates or waveforms [0306] Frequency, duration, and
amount of activity [0307] Frequency, duration, and amount of
fidgeting or restlessness
[0308] In some embodiments, the variability of these variables in
various frequency bands can also be subject to analysis, including
heart rate variability and respiratory rate variability, but also
variability of changes of the shape of the heart and/or respiratory
waveform, changes in the depth of breathing, and changes in the
degree of paradoxical breathing. These may be monitored at specific
times related to questions being asked, statements being made,
and/or specific tasks being performed. Alternatively or
additionally, they may be monitored in subjects going about their
normal activities.
[0309] In some embodiments, the device can distinguish valid
measurement of motion related to heart and/or respiratory activity
as distinct from other detected motion of the subject being
measured and from motion of the background.
[0310] In some embodiments, the Doppler radar-based device operates
as a standalone unit, and can simply be co-mounted with the vital
signs measurement device and/or patient monitoring device. In some
embodiments, the Doppler radar-based device is capable of operating
as a standalone unit, but communicates its outputs to the vital
signs measurement device and/or patient monitoring device. In some
embodiments, the Doppler radar-based device is capable of operating
as a standalone unit, but it is controlled by and communicates its
outputs to the vital signs measurement device and/or patient
monitoring device. In some embodiments, the Doppler radar-based
device does not have a user interface and is typically used in
conjunction with the vital signs measurement device and/or patient
monitoring device, which can control the device and communicates
the outputs of the device to the users.
[0311] In some embodiments, the Doppler-radar based device is
self-contained, with the antennas, radio components, digitization,
and processing contained in the sensing unit. In some embodiments,
the processing is performed on a separate circuit board that is
included in the vital signs measurement device and/or patient
monitoring device. In some embodiments, the processing is performed
on one or more processors in the vital signs measurement device
and/or patient monitoring device that is used to process
information related to other physiological measurements as
well.
[0312] In some embodiments, the cable that connects the Doppler
radar-based device to the vital signs measurement device and/or
patient monitoring device is a USB cable. In some embodiments, a
custom cable connects the Doppler radar-based device to the vital
signs measurement device and/or patient monitoring device. In some
embodiments, the cable is captive in the Doppler radar-based
device, and in some embodiments, the cable can be plugged into and
removed from the device. In some embodiments, the Doppler
radar-based device is powered over the same cable that provides
communications connectivity. In some embodiments, separate cables
are used for power and communication.
[0313] The Doppler radar-based device can cause data to be
transferred between a variety of electronic devices. In some
embodiments, the Doppler radar-based device may communicate its
outputs to the central nurses' station. In some embodiments, the
Doppler radar-based device may communicate its outputs to personal
digital assistants (PDAs) and/or cellular phones, such as smart
phones, that have been programmed to receive the results. In some
embodiments, the Doppler radar-based device may communicate its
outputs to a Doctor's office. In some embodiments, the Doppler
radar-based device may be controlled by any suitable electronic
device, for example, from a central nurses' station, personal
digital assistants, cellular phones, a computer at a Doctor's
office, or any combination thereof.
[0314] In various embodiments, the Doppler radar-based device may
communicate wirelessly with a protocol such as WiFi, Bluetooth,
Zigbee, and/or via cellular networks to another device, such as a
patient monitoring device and or a vital signs measurement device,
and/or to a central station, computer, or database. In some
embodiments, the Doppler radar-based device may communicate results
to a central database and/or computer, which in turn communicates
the results to a patient monitoring device and/or a vital signs
measurement device that is configured to monitor the same
patient.
[0315] In some embodiments, raw data may be streamed from the
sensor to one or more central computing devices and processed in
the one or more central computing devices. In some embodiments,
some or all of the processed data and other outputs of the
processing may be stored on the one or more central computing
devices. In some embodiments, some or all of the processed data and
other outputs of the processing may be streamed back to a device
that is local to the patient or nurse for display. In some
embodiments, the device that is local to the patient or nurse may
be the Doppler radar-based device. In some embodiments, the device
that is local to the patient or nurse may be a vital signs
measurement device and/or patient monitoring device. In some
embodiments, the device that is local to the patient or nurse is a
tablet PC. In some embodiments, the device that is local to the
patient or nurse is a monitor configured to display various
physiological and vital signs parameters.
[0316] In some embodiments, the same radio that is used for Doppler
radar-based sensing can also be used for communications with other
local devices or central systems.
[0317] In some embodiments, the outputs of the Doppler radar-based
device can be forwarded from device to device until reaching a
central system.
[0318] The Doppler radar-based device can advantageously be faced
towards the patient for a measurement. In various embodiments, the
Doppler radar-based device may be mounted with the vital signs
measurement device and/or patient monitoring device in a number of
ways, including mounting directly or indirectly to the cart that
the vital signs measurement device and/or patient monitoring device
is on, mounting directly or indirectly to the vital signs
measurement device and/or patient monitoring device, mounting to
the bed rail, mounting to the ceiling, mounting to the wall,
mounting to another pole, and/or mounting to the foot of the
bed.
[0319] In some embodiments, the mounting mechanism may have a
quick-release mechanism so it can be moved from one mounting
position to another. In some embodiments, the mounting may be
magnetic, such that it can attach to any metallic surface. In some
embodiments, the mounting may be magnetic, such that it can easily
attach to any mounting designed to mount with it. In some
embodiments, the mounting may include a suction cup. In some
embodiments, the mounting may include a clamp. In some embodiments,
the mounting may include a quick release plate on the Doppler
radar-based sensor and a mating piece on the mounting point.
[0320] In some embodiments, the mounting may be easy to move into a
number of different positions. In some embodiments, the mounting
may include a goose neck. In some embodiments, the mounting may
include a universal joint. In some embodiments, the mounting may
include a semi-rigid tube. In some embodiments, the mounting may
include a grip such that when the grip is squeezed, the sensor may
be moved into a number of different positions, but when the grip is
released, the sensor can be locked into a current position.
[0321] In some embodiments, the device may be connected directly or
indirectly to the patient monitoring device and/or vital signs
measurement device. In some embodiments, this connection may be via
a universal joint.
[0322] In some embodiments, the mounting between the patient
monitoring device and/or vital signs measurement device may be
configured such that when the device is properly mounted, the power
and communications are automatically configured such that no
additional cables are necessary. In some embodiments, this mounting
can include a locking socket with a USB connection over which power
and communications can be configured. In some embodiments, the
mounting can include inductive power and communication can be
performed wirelessly, such that the unit can perform all or
substantially all communication wirelessly. In some embodiments, a
battery is included in the mounting hardware, and this battery can
power the Doppler radar-based device.
[0323] In some embodiments in which the Doppler radar-based device
mounts to the same pole as the patient monitoring device and/or
vital signs monitor, the mounting may include a pole clamp that
clamps to the stand, an arm that reaches around the vital signs
measurement device and/or the patient monitoring device and a joint
such that the Doppler radar-based device is beside or above the
vital signs measurement device and/or the patient monitoring
device. In some embodiments, it may be possible to move this
mounting from side to side or behind the vital signs measurement
device and/or the patient monitoring device. In some embodiments,
the arm that reaches around the vital signs measurement device
and/or the patient monitoring device may include a telescoping pole
such that the Doppler radar-based device may be moved up and down
relative to the vital signs measurement device and/or the patient
monitoring device. In some embodiments, the arm that reaches around
the vital signs measurement device and/or the patient monitoring
device may include a sliding track such that the Doppler
radar-based device may be moved up and down relative to the vital
signs measurement device and/or the patient monitoring device.
[0324] In some embodiments, the mounting for the Doppler
radar-based device may be a tension-balanced arm that can be moved
to any position. In some embodiments, the mounting for the Doppler
radar-based device may be a hinged arm similar to that of a desk
lamp.
[0325] In some embodiments, the mounting arm may be powered,
utilizing a screw, hydraulics, cables, and/or a motor to
automatically move the Doppler radar-based device into
position.
[0326] In some embodiments, the Doppler radar-based device may
automatically face towards the subject using beam steering,
direction of arrival algorithms, a motorized rotation, or any
combination thereof. In some embodiments, the optimum direction may
be estimated by sensing the direction and/or relative position of a
thermometer, arm cuff, or other part of the patient monitoring
device and/or vital signs measurement device that is configured to
contact the patient during a measurement. In some embodiments,
there may be a physical attachment between the arm cuff and the
Doppler radar-based sensor unit such that this attachment pulls the
device towards the patient to aim the device.
[0327] In some embodiments, a custom bed frame may be used that the
sensor can easily mount to.
[0328] In some embodiments, the Doppler radar-based device can be
permanently mounted in the bed or on the ceiling and/or wall and
communicates with a central station and/or a local vital signs
measurement device and/or patient monitoring device.
[0329] In some embodiment, Doppler radar-based devices that include
the ability to read RFID tags may be placed throughout the hospital
such that they can track the location of patients and measure the
patients vital signs as they move throughout the hospital.
[0330] In some embodiments, a totally wireless unit can be
implemented by providing wireless power and wireless
communications.
[0331] In some embodiments, the device is solar powered. In some
embodiments, the device is powered kinetically.
[0332] In various embodiments, the device's display may be
co-located with the radar unit, or it may be separate such that the
orientation and/or position of the display may be changed
independently of that of the radar unit. In various embodiments,
the device may use the display of an associated vital signs and/or
patient monitoring device. In various embodiments of a spot check
device, which can display a point-in-time respiratory rate, it may
be possible to alternate between the respiratory rate and the
respiratory waveform used to obtain the rate. In embodiments that
utilize a touch-screen, this may be achieved by touching the number
where the rate is displayed. Alternatively, a separate button may
be used to toggle between the rate and the trace, or waveform. In
various embodiments, it may be possible to zoom in and out on the
waveform or trace. In various embodiments, the zoom may utilize a
multi-touch screen. In various embodiments the zoom may utilize
zoom in and zoom out buttons.
[0333] In various embodiments, a waveform may be displayed and the
user may select the portion of the waveform to use to determine the
rate in a spot check scenario. In some embodiments, the real-time
waveform may be continuously displayed and the user may touch a
button to freeze the waveform and select an interval in which to
determine the rate. In some embodiments, the waveform can un-freeze
after a pre-determined time.
[0334] In various embodiments, when the waveform associated with a
spot check is displayed, the device may display only the portion of
the waveform used to obtain the rate (possibly with portions with
motion removed), it may display the full obtained waveform with the
portion of the waveform used to obtain the rate highlighted. In
some embodiments, the waveform display may include dots on peak
inhalations used to obtain the rate or other parameters.
[0335] In various embodiments, the device may allow the user to
manually input a counted rate.
[0336] In various embodiments, the device may have a button or
touch screen that the user hits at each peak inhalation, and the
device estimates a respiratory rate based on the peak inhalation
times indicated by the user.
[0337] In various embodiments, the height of the wave form on the
screen may auto scale such that the user can see the most detail.
In various embodiments, the height of the waveform on the screen
may be absolute or to scale, such that the user may adjust the
aiming of the device to make the waveform amplitude higher. In
various embodiments in which the depth of breath is calculated, the
height of the waveform on the display may be absolute relative to
the depth of breath. In various embodiments, the scale on the
x-axis (signal power or depth of breath) and the scale on the
y-axis (time) may be selected via the touch screen or via zoon-in
or zoom out buttons.
[0338] In various embodiments, a histogram of recent breath rates
may be displayed. In various embodiments, the number of recent
breath rates or the amount of time included in the histogram may be
selected by the user. In various embodiments the histogram display
may be selected by pressing a button on the device.
[0339] In various embodiments, the device may display trends in the
respiratory rate on a graph that has the rate on the y-axis and
time on the x-axis. In various embodiments, the device may also
indicate the mean and standard deviation of the rate. In various
embodiments, the device may indicate the mean and standard
deviation of the rate by shading a bar that fills the area between
the mean plus one standard deviation and the mean minus one
standard deviation.
[0340] In various embodiments the device, the associated patient
monitor, vital signs device, or any combination thereof may
calculate and display an integrated respiratory status index or an
integrated patient health index.
[0341] In various embodiments, the device may determine a baseline
rate and provide information about changes in the rate from the
baseline rate. In various embodiments, the device may request the
user to enter the baseline rate and then provide information about
changes in the rate from the baseline rate obtained from the
user.
[0342] In various embodiments, the device may provide the
percentage change and/or absolute change in rate and/or average
rate from measurement to measurement and/or at specific time
intervals.
[0343] In various embodiments, trends in the respiratory rate
and/or other physiological variables may be displayed using
Sparklines. Sparklines for respiratory rate may include the words
"respiratory rate" or "respiration rate", a number indicating the
most recently measured respiratory rate value, a line showing the
path of the most recent readings or measurements of respiratory
rate, a band showing the normal range of respiratory rate, or any
combination thereof. In various embodiments, a dot may be placed on
the most recent value, and this dot may be color coordinated with
the number indicating the most recent respiratory rate reading. In
various embodiments, the normal range of respiratory rate may be
based on population averages or may be specific to the patient
being measurement. In various embodiments, the normal range of
respiratory rates may be based on values entered by the user for
the patient being measured. In various embodiments, the normal
range of respiratory rates may be based on patient history.
[0344] In various embodiments, the display may highlight features
of interest, including changes in the waveform, inhale-time to
exhale time ratio, or rate of breathing.
[0345] In various embodiments, the device may detect whether or not
the subject is sleeping. In various embodiments the sleep state may
be included on the display and in the historical data.
[0346] In various embodiments the device may detect and display
heart rate in addition to respiratory rate.
[0347] In various embodiments the device may display an activity
index. In various embodiments the activity index may be calculated
from the amount of motion occurring over time.
[0348] In various embodiments, the device may automatically
reposition and/or electronically steer the radio beam to track a
patient. In various embodiments, the device may reposition and/or
electronically steer the radio beam after each motion event. In
various embodiments the device may reposition and/or electronically
steer the radio beam at predefined intervals.
[0349] In various embodiments, the device may include a camera that
can be used for aiming the device. In various embodiments, the
device may include a display that shows the camera image such that
when the patient's torso fills the display, the user knows that the
device is positioned properly. In various embodiments, a silhouette
or outline of a body may be included in the display to help with
aiming. In various embodiments, the device may include a camera and
use image recognition software to determine the patient positioning
and/or the patient orientation. In various embodiments, the device
may use image recognition to determine motion of the subject. In
various embodiments the device may utilize image recognition
software determining the patient position or orientation to provide
feedback on aiming and/or to automatically reposition the device or
perform electronic beam steering.
[0350] In various embodiments, different measurements, indicators,
or methods of display may be displayed in different sections, such
as quadrants or sextants, of the screen, and by touching one
section, the selected section can expand to full screen. In various
embodiments, it may be possible to change the orientation of
windows including different measurements, indicators, or methods of
display, including but not limited to columns, quadrants, and
rows.
VI. Patient Identification Tag
[0351] In various embodiments, the desired target can wear a tag
that can be used for aiming and/or identification of the desired
target. In some embodiments, the signal strength from the tag can
be used to aid with aiming or otherwise positioning one or more
elements of a system. In some embodiments, a tag can be used in
conjunction with DOA processing to determine the direction of the
tag and to focus the receive beam of a multiple-receiver system in
this direction. In some embodiments, the tag can provide a harmonic
of the transmitted signal or a modulated version of the transmitted
signal. In some of these embodiments, the signal can be obtained
from the tag signal rather than the overall Doppler signal, to
ensure that the signal comes from the desired source. In some
embodiments, a retro-directive antenna can send the signal back in
the same direction using a phased array or corner antennas.
[0352] In various embodiments, an identification (ID) system can be
configured to provide positive patient identification in
conjunction with remote vital signal sensing as illustrated in FIG.
16C. Various embodiments of an ID system can include two basic
components: a reader 1610 and a tag 1612. The tag 1612 can be a
device placed on or near the patient that emits and/or re-emits a
signal. Emitted and/or re-emitted signals can be modulated in such
a way that the signals are encoded with unique identification that
marks that signal as being from a specific tag. In some
embodiments, this unique identification indicates a patient
identification number that corresponds to a patient identifier used
in medical records. The reader 1610 can be a device that receives
the modulated signal from the tag 1612 and identifies the coded
information. In some embodiments, the reader 1610 can also provide
the source signal that the tag 1612 can be configured to modulate
and re-emit. In order for an identification system to link the
vital-sign assessment to a particular patient, it can be sufficient
to ensure that the patient is located within the area in which the
direction-sensitive and range-sensitive sensor can measure. For
example, some direction-sensitive and/or range-sensitive sensors
can obtain reliable measurements within a radius of no more than
about 1,000 feet, 500 feet, 200 feet, 100 feet, 50 feet, 25 feet,
or 10 feet. In some embodiments, direction sensitivity in a
remote-sensing radar can be achieved through use of a directional
antenna that can be insensitive and/or unresponsive to signals
outside of a limited angle range in two dimensions. For example,
the limited angle range can be less than about 270, 240, 210, 180,
150, 120, 90, 60, 45, 30, 20, 15, 10, or less degrees. In various
embodiments, range sensitivity can be limited through power
sensitivity and/or range-gating of pulse signals. A
location-specific ID system can typically have an active area
within of this three dimensional space of sensor sensitivity.
[0353] In some embodiments, the tags can be encoded with a patient
identification number and/or another unique identifier of the
patient. In some embodiments, the vital signs monitor can access
patient information (such as name, etc.) based on information
obtained from this tag and display patient information for the
patient being measured on the display. In some embodiments, the
vital signs monitor can transmit vital signs information with the
patient identification number such that in a central nursing
station, the vital signs are displayed with the patient
identification number, and/or such that the vital signs are stored
within or associated with the patient's electronic medical
record.
[0354] In some embodiments, at the initiation of a continuous
measurement, the nurse can synchronize the vital signs monitor with
the tag worn by the patient, such that the monitor can only
monitor, display, transmit, and/or record vital signs when that tag
is in the field of view, until a new measurement is initiated, with
a new tag.
[0355] FIG. 16D shows an embodiment of an active tag 1612 emitting
a signal modulated with a unique ID signature that is received by
the reader device 1610. In this embodiment, the reader 1610 has a
directional antenna that detects the tag's 1612 signal from a
specific angle range. In various embodiments, the power of the tag
1612 can be adjusted to limit the range in which the tag can be
sensed such that the ID area is the same area sensed by the
vital-sign monitor.
[0356] FIG. 16E shows a tag 1612 receiving a signal and either
re-emitting the signal modulated with unique ID information
(passive) or emitting a new signal (active). In various
embodiments, in order for the ID to be location specific, the
transmit and/or the receive apparatus can be directional. In
various embodiments, the tag 1612 can either emit or re-emit in an
omni-directional fashion or utilizing a retro-directive method such
as a corner reflector or a phased array.
[0357] In some embodiments, a signal can be transmitted by an
exciter, received by the tag, re-emitted in an omni-directional
direction, with the signal modulated by the tag in such a way that
there is identifiable information in the signal, and then detected
by a receiver. In some embodiments, the tag can reflect the signal
back to the source using, for example, a retro-directive array or a
corner reflector. In some embodiments, the exciter can be
co-located with the receiver. In some embodiments, the exciter and
receiver both included within a transceiver architecture. In some
embodiments, modulation can include amplitude modulation, phase
modulation, frequency modulation, or any combination thereof of the
carrier signal. In some embodiments, the tag can return a signal
that has orthogonal polarization for linear polarization or counter
rotation, for circular polarization. In some embodiments, the tag
can return a signal that is a harmonic of the carrier signal. In
some embodiments, digital information can be modulated by methods
including, but not limited to one or more of: pulse width, pulse
delay, pulse amplitude, and pulse density.
[0358] FIG. 16F is similar to FIG. 16E in which the tag is
configured to receive a signal and emits or re-emits a modulated
signal with a unique ID. However, FIG. 16F is a more general form
in which the exciter 1614 and the reader 1610 are separate and not
necessarily co-located. In this case both the exciter 1614 and the
reader 1610 can be directional in order to make the affective area
specific to the area sensed by the vital-sign monitor. In some
embodiments, the exciter and the reader may not be co-located.
[0359] In some embodiments of an active tag, a battery-operated
RFID tag can be sensed by a reader with a directional antenna
co-located with vital-sign sensor.
[0360] In some embodiments, an infra-red LED tag pulses a unique
ID, which can be read by an IR-sensitive camera. This camera data
can be analyzed to restrict vital-sign sensing to periods when the
LED is in a specific area in the camera's view. In various
embodiments, the camera can be either ceiling mounted or co-located
with the sensor.
[0361] In some embodiments, an ultra-sonic tag can be utilized
which has a modulated sonic signal at a frequency above that which
humans can hear. In some embodiments, ultrasonic microphones can be
placed for triangulation to position of tag, and the tag position
can be analyzed to indicate whether it is within the range and
angle from which the radar-based vital signs sensor can
operate.
[0362] In some embodiments, the reader is located with the patient
and identifies coded information in an RF signal associated with
the vital-sign sensor. The reader can respond with an
omni-directional signal indicating proper ID acquisition. In
various embodiments, this response signal can be in accordance with
communication protocols that include, but are not limited to: IEEE
802.11 (wifi), Bluetooth, zig-bee, ultra-sonic, infra-red and/or
ISM band RF radiation.
[0363] In some embodiments, a tag can re-emit RF radiation from the
vital-sign sensor's transmitter modulated based on its unique ID.
In various embodiments, the reader, with a directional antenna, can
be ceiling-mounted, floor mounted, or co-located with the
vital-sign sensor. In some embodiments, the reader can have a
directional antenna. In some embodiments, the tag can re-emit an
omni-directional signal.
[0364] In some embodiments, a camera can be mounted on the ceiling
or co-located with the sensor, and use facial recognition
algorithms to indicate whether the patient is in specific areas of
a hospital room before recording vital-signs. In some embodiments,
when the healthcare practitioner initiates the measurements, he or
she can synchronize the sensor with the face of the patient.
[0365] In some embodiments, a camera is mounted on the ceiling or
co-located with the sensor, and the patient's tag and/or hospital
gown can have a unique pattern that can be deduced by the
image-processing algorithms.
[0366] Some embodiments of the system can use a Doppler radar-based
identification system that can provide positive patient
identification while acquiring vital sign signals. In some
embodiments, the identification system can provide alternative ways
of acquiring physiological signals. FIG. 16G illustrates the
concept of enabling positive identification (ID) using a tag
attached on the patient. The tag reader, or reader unit 1620, can
transmit a continuous wave (CW) signal towards the subject 1622
using a somewhat directive antenna beam illuminating the subject
1622. As the signal is reflected from the subject's thorax, its
phase can be modulated proportionally to the thorax's cardiac
and/or respiratory motion. When this signal is received and
downconverted, there can be a baseband Doppler signal at or around
the cardiopulmonary signal frequency. In various embodiments, the
ID tag 1624 can be attached to the patient's upper body, either
attached to the clothing or adhered to the skin of the patient with
an adhesive. In some embodiments, the tag 1624 can be battery
operated; however, it can be passive in the sense that it cannot
generate transmit signals on its own, but when the signal
transmitted by the reader unit 1620 illuminates the tag 1624, the
tag 1624 can modulate the backscatter by changing the reflection
coefficient from the antenna at a programmed frequency. In some
embodiments, the reflection coefficient from the antenna can be
changed by periodically connecting the antenna to a load by
controlling the bias current of a diode connecting the antenna and
a load, resulting in generation of sidebands that carry ID
information. In some embodiments, a local battery on the tag can
facilitate the periodic connection of the antenna to a load.
[0367] One embodiment of the passive transponder RFID technology is
shown in FIG. 16H. The illustrated embodiment is a crystal 1632
based two-way radio powered by a watch battery. This tag is passive
in the sense that it does not typically generate a signal by
itself, however a battery is typically used to power a
microprocessor 1626 and provide a modulating current to the diode.
The backscatter from the tag can be modulated by the bias current
to the diode 1628, which can change the impedance "seen" by the tag
antenna 1630, and thus the power reflected from the antenna. The
modulating current can be produced by a microprocessor 1626 driven
by a low frequency clock, (in some embodiments, the clock is in the
10 kHz range). Thus, the modulated backscatter can appear at the
sideband frequency (in some embodiments, in the 10 kHz range), and
can be easily separated from the baseband Doppler signal through
filtering in the digital domain. The data acquisition sampling rate
can advantageously be greater than twice the sideband frequency
range (in some embodiments, 20 kHz) to avoid aliasing in accordance
with Nyquist's Theorem. In some embodiments in which a low-IF
architecture is used, the sampling rate can be selected considering
that the sampling rate is preferably at least double the low IF
frequency+double the sideband frequency. In some embodiments, the
tag antenna 1630 is omni-directional to ensure that the backscatter
can be detected by the reader if the subject changes position. In
some embodiments, multiple tags can be used to provide signal
diversity, for example, on the front and back of the subject, but
in other embodiments, only one tag is utilized. In some
embodiments, the tag can convey a unique identifier of a patient on
carrier signal and/or reflected signal by one of several methods,
including but not limited to: frequency modulation, frequency shift
keying (FSK), pulse width modulation, and phase shift keying (PSK).
In some embodiments, these modulated reflected signals are then
demodulated and converted to binary identification numbers.
[0368] In some embodiments, unique identifier associated with a
patient, such as the patient's ID number, can be encoded on the
reflected carrier signal by using conventional modulation methods
including but not limited to PSK or FSK modulation. In some
embodiments, codes can be set by several bits including pilot bits
for both cases. In some embodiments, pilot bits can let the system
know the first bit of the patients' ID number and can be
consecutive three bits with value one or high. In case of PSK, a
fixed offset frequency of more than one cycle can comprise one bit
of code bit. In some embodiments, the value of each bit can be
assigned by shifting the phase of modulated signal from 0 to 180
degree. In some embodiments using the system illustrated in FIG.
16H, PSK can be achieved by switching the load attached to the
antenna via the diode to provide the phase shift. In some
embodiments, the bit values can change whenever the current bit
phase is 180 degrees different from the previous bit. In some
embodiments utilizing FSK, two different frequencies can be used
for modulating the reflected signal, one of which represents zero
while the other does one. In some embodiment using the system
illustrated in FIG. 16H, this can be achieved by switching the
diode at the crystal frequency and half the crystal frequency for a
fixed period. In other embodiments using the system illustrated in
FIG. 16H, four frequencies can be used to provide 2-bit data. In
other embodiments using the system illustrated in FIG. 16H, more
than 4 frequencies can be used.
[0369] In some embodiments, the same radar front-end can be used to
detect both the ID information appearing in the sidebands, and the
Doppler shift generated by the subject's physiological motion, from
the portion of the signal reflected by the thorax and not the tag
as shown in FIG. 16I. One difference between the ID information and
the Doppler shift generated by physiological is the bandwidth,
which can affect the required sampling rate. The sampling rate for
the combination radar sensor-ID reader is preferably adequate for
detection of the sidebands generated by the tag and for the
baseband Doppler shift generated by the subject's physiological
motion. After complex down-conversion, the sidebands can appear at
a low IF frequency (in some embodiments, this can be in the 10-kHz
range--the same or substantially the same frequency as the crystal)
that can be digitized and further demodulated in digital domain.
The baseband Doppler shift can be near DC, at frequencies below
10-Hz. The baseband signal conditioning can be essentially the same
for both the tag reader and the direct-conversion Doppler radar
sensor of physiological motion, but in the tag reader system, it
may need to accept signals that are sufficiently wideband to
include both the baseband Doppler signal and the sidebands
generated by the tag. In some embodiments, the signal generated by
the tag can have a much lower power than that reflected from the
torso, in which case the dynamic range of the receiver is
preferably adequate to detect both signals. In various embodiments,
this can include one or more of the following methods: AC-coupling
the signal to remove DC offsets before amplification and using a
high-resolution analog-to-digital converter; applying a method of
DC cancellation or DC compensation in analog processing before a
high-gain stage and using a high-resolution analog-to-digital
converter; separately processing the sideband and the baseband
Doppler signal such that each has appropriate gain and filtering;
and/or using a high resolution analog-to-digital converter.
[0370] In some embodiments, in addition to the identification
signals provided by the tag, it is also possible to obtain signals
about physiological motion from the Doppler shift of the sideband
signals generated by the tag, referred to herein as the sideband
Doppler signal. Once the signal is digitized, the sideband signals
(those generated by the motion of the tag) can be separated from
the baseband Doppler signals (those reflected by the thorax without
the tag). In some embodiments, the sideband Doppler signal can be
digitally downconverted to baseband, and processed substantially
the same way that the baseband Doppler signal is processed. Since
the ID tag itself can be attached to the moving surface, signals
reflected from the tag antenna can contain a similar Doppler shift
as that produced by the moving chest. If there were no modulation
on the tag, these two signals would add and it would be challenging
to separate them. However, since the tag backscatter can be shifted
in frequency by modulating diode bias current, the Doppler shift,
as well as the ID information, can appear on these sidebands. Since
the modulated backscatter from the tag (sideband Doppler shift) can
originate only from the chest region physically attached to the
tag, and the carrier Doppler shift results from the illumination of
a larger area that can include the hands, arms, shoulders, and
legs, it is expected that two signals can exhibit subtle
differences. In some cases, the modulated backscatter can be more
immune to fidgeting motion, since there can be fewer potential
sources of non-cardiopulmonary motion attached to the tag. In some
embodiments, the Doppler-shift signal obtained from the tag can be
compared with the Doppler shift signal obtained from the non-tag
reflections. In some embodiments, significant differences in the
two signals can indicate non-cardiopulmonary motion in the signal
obtained with the non-tag reflections. In some embodiments, the two
signals can be compared with a cross correlation function, and the
degree of correlation between the signals can be used to determine
whether or not to indicate non-cardiopulmonary motion. In some
embodiments, the Doppler-shift signal obtained from the tag
reflection can be used for physiological processing. An additional
advantage of the sideband signals can be that they typically do not
suffer from distortion due to AC coupling, in embodiments where an
AC-coupled receiver is used, and they can also be less affected by
1/f noise.
[0371] In some embodiments, a desired or designated subject can be
continuously monitored within a predefined boundary. For example,
the desired or designated subject can be continuously monitored in
a home environment or any portion thereof. This can be
accomplished, for example, when there is adequate coverage of all
rooms with one or more reader and the subject is wearing a tag.
[0372] FIG. 16J is a flow chart illustrating an embodiment of the
identification-reading and vital signs signals processing of the
sideband signals. In this embodiment, the ID code is encoded on the
signal by the RFID tag, using fixed-length PSK codes at a fixed
offset frequency. In this embodiment, the encoded signal can be
modulated on the signal reflected by the RF tag's microprocessor,
resulting in a sideband signal offset from the carried frequency by
the frequency of the PSK modulation. Since the amplitude of the
correlation coefficient can be proportional to the position or
delay of the reflected encoded signal, the amplitude variation of
the correlation coefficient can be used to provide vital signs
which can be used for information diversity or confirmation when
obtaining vital signs from the baseband Doppler signal
[0373] One embodiment of a respiration rate spot checker is
illustrated in FIG. 18. The system includes a radar-based
physiological sensor 1801 similar to the various embodiments
described above, a computational unit, and a display unit. In
various embodiments, the computational unit and the display unit
can be housed together in single housing 1802 (e.g., a laptop, a
handheld computer, a PDA, etc.). The sensor 1801 can communicate
with the computation unit and/or the display unit wirelessly or
over a wired connection using the various communication protocols
discussed above. In various embodiments, the sensor 1801, the
computation unit and the display unit can be housed together in a
single housing. In certain embodiments, the sensor 1801 and the
computational unit can be housed together in single unit and the
display unit can be separate.
[0374] In various embodiments, after the signal is sampled by the
analog to digital converter (ADC), it can transmitted over a wired
or wireless communication link (e.g., Bluetooth, USB, etc.) to one
or more processors that performs signal processing.
[0375] In some embodiments, the radar sensor can include multiple
antennas, each with a receiver, such that it can determine the
direction of a signal source. In some embodiments, this can be used
to determine the direction of the target and to provide feedback to
the user on how to better aim the device toward the target. In some
embodiments, this multiple-receiver sensor can be used in
conjunction with a radio-frequency tag, such that the sensor can
determine the direction of the tag and provide feedback to the user
on how to better aim the device toward the tag. In some
embodiments, a multiple antenna sensor used in conjunction with a
radio frequency tag can differentiate or separate the desired
target's signal from interference with a software defined smart
antenna technique.
[0376] In some embodiments, the tag can be constructed using a
commercially available Bluetooth module for the tag and the reader.
A liquid resistant housing can be designed to encase the Bluetooth
module, coin cell battery, voltage upconverter/regulator, LED
indicator, an activation circuit, or any combination thereof. The
housing can have a slot on either side of the tag so that the
housing can be securely clipped to the patient's clothing or worn
with a wrist strap. In some embodiments, the activation circuit can
preserve the coin cell battery until the tag is activated by
pressing a water resistant, indented button, for example, with a
pen tip. In some embodiments, the tag can also have a single, 3
color LED that flashes blue when it has a Bluetooth connection,
flashes green every 10 seconds when the tag is activated and
flashes red every 10 seconds when the battery is low.
VII. Terminology
[0377] Conditional language used herein, such as, among others,
"can," "could," "might," "e.g.," and the like, unless specifically
stated otherwise, or otherwise understood within the context as
used, is generally intended to convey that certain embodiments
include, while other embodiments do not include, certain features,
elements and/or states. Thus, such conditional language is not
generally intended to imply that features, elements and/or states
are in any way required for one or more embodiments or that one or
more embodiments necessarily include logic for deciding, with or
without author input or prompting, whether these features, elements
and/or states are included or are to be performed in any particular
embodiment.
[0378] Depending on the embodiment, certain acts, events, or
functions of any of the methods described herein can be performed
in a different sequence, can be added, merged, or left out all
together (e.g., not all described acts or events are necessary for
the practice of the method). Moreover, in certain embodiments, acts
or events can be performed concurrently, e.g., through
multi-threaded processing, interrupt processing, or multiple
processors or processor cores, rather than sequentially.
[0379] The various illustrative logical blocks, modules, circuits,
and algorithm operations described in connection with the
embodiments disclosed herein can be implemented as electronic
hardware, computer software, firmware, or combinations of the same.
To clearly illustrate this interchangeability of hardware and
software, various illustrative components, blocks, modules,
circuits, and operations have been described above generally in
terms of their functionality. Whether such functionality is
implemented as hardware or software depends upon the particular
application and design constraints imposed on the overall system.
The described functionality can be implemented in varying ways for
each particular application, but such implementation decisions
should not be interpreted as causing a departure from the scope of
the disclosure.
[0380] The various illustrative logical blocks, modules, and
circuits described in connection with the embodiments disclosed
herein can be implemented or performed with a general purpose
processor, a digital signal processor (DSP), an application
specific integrated circuit (ASIC), a field programmable gate array
(FPGA) or other programmable logic device, discrete gate or
transistor logic, discrete hardware components, or any combination
thereof designed to perform the functions described herein. A
general purpose processor can be a microprocessor, but in the
alternative, the processor can be any conventional processor,
controller, microcontroller, or state machine. A processor can also
be implemented as a combination of computing devices, e.g., a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration.
[0381] The blocks of the methods and algorithms described in
connection with the embodiments disclosed herein can be embodied
directly in hardware, in a software module executed by a processor,
or in a combination of the same. A software module can reside in
RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory,
registers, a hard disk, a removable disk, a CD-ROM, or any other
form of computer-readable storage medium known in the art. An
illustrative storage medium is coupled to a processor such that the
processor can read information from, and write information to, the
storage medium. In the alternative, the storage medium can be
integral to the processor. The processor and the storage medium can
reside in an ASIC. The ASIC can reside in a user terminal. In the
alternative, the processor and the storage medium can reside as
discrete components in a user terminal.
[0382] While the above detailed description has shown, described,
and pointed out novel features as applied to various embodiments,
it will be understood that various omissions, substitutions, and
changes in the form and details of the devices or algorithms
illustrated can be made without departing from the spirit of the
disclosure. As will be recognized, certain embodiments of the
inventions described herein can be embodied within a form that does
not provide all of the features and benefits set forth herein, as
some features can be used or practiced separately from others. The
scope of certain inventions disclosed herein is indicated by the
appended claims rather than by the foregoing description. All
changes which come within the meaning and range of equivalency of
the claims are to be embraced within their scope. Although certain
embodiments and examples are disclosed above, inventive subject
matter extends beyond the specifically disclosed embodiments to
other alternative embodiments and/or uses and to modifications and
equivalents thereof. Thus, the scope of the claims appended hereto
is not limited by any of the particular embodiments described. For
example, in any method or process disclosed herein, the acts or
operations of the method or process can be performed in any
suitable sequence and are not necessarily limited to any particular
disclosed sequence. Various operations can be described as multiple
discrete operations in turn, in a manner that can be helpful in
understanding certain embodiments; however, the order of
description should not be construed to imply that these operations
are order dependent. Additionally, the structures, systems, and/or
devices described herein can be embodied as integrated components
or as separate components. For purposes of comparing various
embodiments, certain aspects and advantages of these embodiments
are described. Not necessarily all such aspects or advantages are
achieved by any particular embodiment. Thus, for example, various
embodiments can be carried out in a manner that achieves or
optimizes one advantage or group of advantages as taught herein
without necessarily achieving other aspects or advantages as can
also be taught or suggested herein. Thus, the invention is limited
only by the claims that follow.
* * * * *