U.S. patent application number 12/871323 was filed with the patent office on 2011-04-14 for system and process for non-invasive collection and analysis of physiological signals.
This patent application is currently assigned to WellAWARE Systems, Inc.. Invention is credited to Majd Alwan, Robin Felder, Steve Kell, David C. Mack, Beverly Turner, Sarah Wood.
Application Number | 20110087113 12/871323 |
Document ID | / |
Family ID | 34636338 |
Filed Date | 2011-04-14 |
United States Patent
Application |
20110087113 |
Kind Code |
A1 |
Mack; David C. ; et
al. |
April 14, 2011 |
System and Process For Non-Invasive Collection and Analysis of
Physiological Signals
Abstract
A system and method for detecting, monitoring and analyzing
physiological characteristics. Signals from a subject are acquired
from a suite of sensors, such as temperature, carbon dioxide,
humidity, light, movement, electromagnetic and vibration sensors,
in a passive, non-invasive manner. The signals are processed and
physiological characteristics are isolated for analysis. The system
and method are to analyze sleep patterns, as well as to prevent bed
sores or detect conditions such as illness, restless leg syndrome,
periodic leg movement, sleep walking, or sleep apnea. However,
numerous other applications of the invention are also
disclosed.
Inventors: |
Mack; David C.;
(Charlottesville, VA) ; Kell; Steve; (Keswick,
VA) ; Alwan; Majd; (Charlottesville, VA) ;
Felder; Robin; (Charlottesville, VA) ; Turner;
Beverly; (North Garden, VA) ; Wood; Sarah;
(Lovingston, VA) |
Assignee: |
WellAWARE Systems, Inc.
Glen Allen
VA
|
Family ID: |
34636338 |
Appl. No.: |
12/871323 |
Filed: |
August 30, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12168413 |
Jul 7, 2008 |
7785257 |
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12871323 |
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10974027 |
Oct 27, 2004 |
7396331 |
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12168413 |
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60514677 |
Oct 27, 2003 |
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Current U.S.
Class: |
600/481 ;
600/300; 600/529; 600/595 |
Current CPC
Class: |
A61B 5/113 20130101;
A61B 5/6892 20130101; A61B 5/11 20130101; A61B 5/4818 20130101;
A61B 5/447 20130101; A61B 5/1126 20130101; A61B 5/024 20130101;
A61B 2562/046 20130101; A61B 5/0816 20130101; A61B 2562/0247
20130101 |
Class at
Publication: |
600/481 ;
600/300; 600/595; 600/529 |
International
Class: |
A61B 5/11 20060101
A61B005/11; A61B 5/00 20060101 A61B005/00; A61B 5/0205 20060101
A61B005/0205; A61B 5/08 20060101 A61B005/08 |
Claims
1-20. (canceled)
21. A non-invasive system for assessing physiological
characteristics of a subject, said system comprising: a network of
sensors configured to provide electronic output signals indicative
of at least one sensed condition, said network including at least
one passive sensor selected from the group consisting of vibration,
temperature, carbon dioxide, humidity, electromagnetic and light
sensors; data acquisition circuitry for collecting the electronic
signals output from said network of sensors; a processor configured
to process the electronic signals; and an output device for
outputting the processed signals into human-readable data
indicative of at least one condition of a subject, wherein said
human-readable data comprises a subject restlessness rate.
22. The system of claim 21 wherein said processor further includes
an amplifier configured to amplify at least one of the electronic
signals.
23. The system of claim 22, wherein said processor further includes
first and second filters configured to receive the amplified
electronic signals to produce two processed electronic signals,
wherein said first filter includes a band pass filter and said
second filter includes a low pass filter and a high pass
filter.
24. The system of claim 21, wherein at least some of the sensors
are passive sensors requiring no active involvement from the
subject and no direct attachment to the subject.
25. The system of claim 24, wherein all of the sensors are passive
sensors requiring no active involvement from the subject and no
direct attachment to the subject.
26. The system of claim 21, wherein the human-readable data
comprises an obstructive sleep apnea count.
27. The system of claim 21, wherein at least a portion of said
network of sensors is incorporated in one of a mattress pad, bed,
couch, or chair.
28. The system of claim 27, wherein said mattress pad consists of
sleeves and fixtures to accommodate sensor pads within said sensor
network including at least one passive sensor selected from the
group consisting of vibration, temperature, carbon dioxide,
humidity, electromagnetic and light sensors.
29. The system of claim 21, wherein saturation of at least one of
said sensors allows differentiation between gross movement and
acute movements.
30. The system of claim 29, wherein gross movement includes
physical repositioning and acute movement includes cardiac and
respiratory events.
31. The system of claim 30, wherein at least one of the gross
movement and acute movement are interpreted to prevent development
of pressure ulcers or bed sores in the patient.
32. The system of claim 21, wherein said sensors are from the group
including piezoelectric, fiber optic, or load cell.
33. The system of claim 21, wherein said data acquisition circuitry
further comprises a user activated privacy flag to flag for
omission output signals from said network of sensors.
34. A non-invasive system for assessing physiological
characteristics of a subject, said system comprising: a network of
sensors configured to provide electronic output signals indicative
of at least one sensed condition, said network including at least
one passive sensor selected from the group consisting of vibration,
temperature, carbon dioxide, humidity, electromagnetic and light
sensors; data acquisition circuitry for collecting the electronic
signals output from said network of sensors; a processor configured
to process the electronic signals, wherein a first signal is
acquired that corresponds to a respiratory rate on a
breath-by-breath basis of the subject and a second signal is
acquired that corresponds to a heart rate on a beat-by-beat basis
of the subject; and an output device for outputting the processed
signals into human-readable data indicative of at least one
condition of a subject, wherein the human-readable data comprises a
subject restlessness rate.
35. The system of claim 34, wherein said processor further includes
first and second filters that generate two processed electrical
signals via frequency filtering, wherein said first filter includes
a band pass filter and said second filter includes a low pass
filter and a high pass filter.
36. The system of claim 35, wherein said first filter receives
signals indicative of chest movement and said second filter
receives signals indicative of heart movement.
37. The system of claim 36, wherein and said processor includes an
algorithm to generate a heart rate and a respiratory rate based on
processed electronic signals output at or below about 50 Hz.
38. A non-invasive system for assessing physiological
characteristics of a subject, said system comprising: a network of
sensors configured to provide electronic output signals indicative
of at least one sensed condition, said network including at least
one passive sensor selected from the group consisting of vibration,
temperature, carbon dioxide, humidity, electromagnetic and light
sensors, wherein said network of sensors can saturate when gross
movement occurs; data acquisition circuitry for collecting the
electronic signals output from said network of sensors; a processor
configured to process the electronic signals, wherein a first
signal is acquired that corresponds to a respiratory rate on a
breath-by-breath basis of the subject and a second signal is
acquired that corresponds to a heart rate on a beat-by-beat basis
of the subject; and an output device for outputting the processed
signals into human-readable data indicative of at least one
condition of a subject; wherein said system is adapted to assess
the subject's quality of sleep and wherein: said data acquisition
circuitry is configured to collect the electronic signals output
from said network of sensors indicative of heart rate and/or
respiratory rate; and said processor includes an algorithm to
generate a heart rate and a respiratory rate based on processed
electronic signals and an algorithm for determining sleep staging
and aspects of sleep quality based on the heart rate, the
respiratory rate, a variability of the heart rate, a variability of
the respiratory rate and a subject restlessness rate.
39. The system of claim 38, wherein at least part of the system is
incorporated in one of a mattress pad, bed, couch, or chair.
40. The system of claim 38, wherein the human-readable data
comprises an obstructive sleep apnea count.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation of and claims the benefit
of U.S. patent application Ser. No. 10/974,027 filed Oct. 27, 2004,
which claims the benefit of U.S. Provisional Application No.
60/514,677, filed Oct. 27, 2003, the contents of which are
incorporated by reference herein in their entirety.
FIELD OF THE INVENTION
[0002] The invention relates to non-invasive analysis of
physiological signals and more particularly to a system and process
for detecting, collecting and processing physiological
characteristics acquired by a suite of sensors embedded in a
person's environment.
BACKGROUND OF THE INVENTION
[0003] There are large amount of people affected by sleep related
conditions who could benefit from knowing more about their sleep
habits. Some research indicates that 40% of all American adults
suffer from some kind of sleep disorder, while about 70 million
Americans are chronically sleep deprived. Many feel that little
substantial improvement can be made to correct their problems,
since sufferers often do not discuss the problem with their
physician.
[0004] The current gold standard for sleep research is known as
polysomnography (PSG), which involves at least the recording of an
electroencephalogram (EEG), a measurement of brain waves, an
electrooculogram (EOG), a measurement of muscle activity in the eye
area, and an electromyogram (EMG), a measurement of muscle activity
in specific areas such as the arm or leg. These waveforms allow a
doctor to assess a patient's sleep quality. All of these electrode
hook-ups prove valuable in obtaining relevant information used to
assess sleep quality, but require patients to have electrodes
attached to their bodies.
[0005] In an effort to provide a less intrusive way to study sleep
on a longer-term basis, actigraphs have been developed. These
devices can be attached to any of the limbs and provide movement
data based on the same principles behind accelerometers. They are
also used in activity studies and can provide twenty-four hour
monitoring of the subject. This type of sensor, however, has its
limitations in acquiring data that can be interpreted definitively
to provide a good assessment of sleep quality. Researchers are
dependent on patient journals to help correlate the data recorded
on the actigraph and it is hard to distinguish different events
that can occur throughout the night. Patient non-compliance in
journaling adds to the confusion. In addition, many of the problems
researchers have interpreting results from actigraphs are a direct
result of the one-dimensional nature of the data recorded. For
example, if a patient places their hand on their chest or under
their head, the motion data recorded by the actigraph can be
misinterpreted or could potentially hide important events.
[0006] To provide an even less intrusive approach that does not
involve equipment attached to the subject, different physiological
parameters must be examined. One way to look at cardiologic and
respiratory events is through a technique called
ballistocardiography (BCG). BCG involves the study of the cardiac
system by measuring forces related to the contraction and
relaxation of the heart, along with forces propagated throughout
the vascular system. It has been shown that cardiac forces
correlate to life duration and susceptibility to ischemic heart
disease. Additionally, it has been shown that the average force
seen in the BCG reduces as a person ages. Unfortunately, initial
high expectations and hopes set forth in the mid-20th century for
this technology simply resulted in disappointments because
inadequate analysis tools were available. In addition, the
electrocardiogram (ECG) quickly usurped this technology as a more
practical way to measure cardiac function.
[0007] Now that data acquisition systems are commonplace and the
cost of personal computers have been greatly reduced in the last
decade, analysis of the BCG data is no longer an insurmountable
hurdle. In fact, a team from Stanford University designed a system
called "SleepSmart" that uses an array of pressure and temperature
sensors to acquire physiological data. These sensors are embedded
in a mattress and can detect position, temperature, sound,
vibration and movement, with other sensors optional for additional
information. They determined that a sheet of piezoelectric film was
best for implementation of their design. However, they were only
able to obtain results similar to a static charge sensitive bed in
that they were able to obtain good measures of breathing waveforms,
but unable to obtain reliable heart rate measures, thus deeming the
technology insufficient for medical application.
[0008] Previous efforts to passively, i.e., without the active
involvement of the subject and without direct connection of sensors
to the subject, collect physiological data have been expensive, as
processing ability for analysis sufficient for medical applications
was inefficient and expensive. Isolating the appropriate components
from a signal required complicated circuitry and processing. This
complexity added expense without a proportional increase in
accuracy and reliability of the outputs. Due to the number of
people that suffer from sleep related disorders, as well as the
need for non-invasive collection of medical and other data in
applications such as, monitoring the conditions of sick, old or
bedridden patients and prenatal infants, there is need for a more
efficient and accurate passive data collection and analysis system
to monitor various conditions of subjects. In particular, there is
a need for such a passive system in hospitals, nursing homes,
assisted living facilities, sleep labs or home sleep study centers,
doctors' offices, health monitoring stations and the like.
BRIEF SUMMARY OF THE INVENTION
[0009] The invention avoids the disadvantages and drawbacks of the
prior art and/or satisfies the need for more efficient and accurate
passive data collection and analysis by providing for improved
passive data collection, signal processing and analysis of the
psychological characteristics. In particular, the invention
satisfies this need by providing a system and method employing a
network of sensors at least some of which passively detect
physiological characteristics of the subject. The output from the
network of sensors is processed in a novel manner using a series of
signal filters to isolate signals for the desired physiological
characteristics such as heart rate and breathing rate. The
physiological characteristics are then analyzed for research and/or
diagnosis.
[0010] The invention thus provides improved signal processing while
increasing efficiency without adding exorbitant costs. The
processing is computationally and cost effective, thereby allowing
a more comprehensive analysis of physiological characteristics of a
subject. Further, opportunities for studies and/or diagnostics to
be performed on subjects are increased due to the improved, cost
effective signal processing.
[0011] The invention may be implemented in a number of ways.
According to one aspect of the invention a non-invasive system for
assessing physiological characteristics of a subject, is provided.
The system includes a network of sensors configured to provide
electronic output signals indicative of at least one sensed
condition. The network includes at least one passive sensor
selected from the group consistently of vibration, temperature,
carbon dioxide, light and electromagnetic sensors. Data acquisition
circuitry may be provided for collecting the electronic signals
output from the network of sensors. A processor is configured to
process the electronic signals and includes an amplifier configured
to amplify at least one of the electronic signals, and first and
second filters configured to receive the amplified electronic
signals to produce two processed electronic signals. The first
filter may include a band pass filter and the second filter may
include a low pass filter and a high pass filter. An output device
may also be provided for outputting the processed signals into
human-readable data indicative of at least one condition of a
subject.
[0012] According to a further aspect of the invention, the system
includes an array of ultra-sensitive sensors capable of detecting
movements induced by cardiac and respiratory forces, and is
particularly adapted to assess the quality of sleep of a subject.
The human-readable data comprises at least one of a heart rate
waveform and a respiratory waveform, an obstructive sleep apnea
count, and a subject movement percentage.
[0013] According to an additional aspect of the invention, at least
one sensor may be formed from a matrix of momentary binary
pressure-sensitive contact switches that are embedded in a mattress
pad, block of foam, bed sheet, or a chair, said matrix providing
electronic signals corresponding to a positional map of the
subject.
[0014] According to a further exemplary aspect of the invention, a
method of analyzing physiological characteristics of a subject
including cardiac and/or respiratory parameters on a beat-by-beat
or breath-by-breath basis is provided. The method may include
various steps including passively detecting physiological
characteristics through a network of sensors requiring no conscious
input by the subject and being capable of providing electronic
output signals indicative of a sensed condition. At least one
sensor may be selected from the group consisting of vibration,
position, temperature, relative humidity, carbon dioxide, light and
electromagnetic sensors. The electronic signals output from the
network of sensors may be collected for processing. During the
processing step, at least one of the electronic signals may be
amplified and fed to first and second filters to produce two
processed electronic signals. The first filter may include a band
pass filter and the second filter may include a low pass filter and
a high pass filter. Finally, the processed signals may be output
into human-readable format, such as waveforms indicative of cardiac
and/or respiratory conditions.
[0015] In addition to sleep data obtained in a bed or chair, the
invention may be used for gathering general physiologic or neural
parameters of a subject in a chair or bed while subjects are awake
during their daily living activities. Such a system could provide
important information. For example, measuring how quiet or active a
subject is behaving in response to various external stimuli could
give important clues as to physiologic reactivity, mood, or
disposition. Physiologic responses to entertainment, socialization,
and other mental or physical stimuli of any nature whether the
stimuli is visual, tactile or audible could be important for
assessing physical or mental health. General health assessments may
be performed based on the physiologic responses. In addition,
responses could give important clues as to how consumers behave in
response to the aforementioned stimuli. Thus, the invention is
applicable not only for gathering and interpreting data during
sleep, or for diagnosing health conditions of sick people, but also
for gathering and interpreting data related to normal human
responses during all hours of wakefulness to external visual,
tactile, audible stimuli, or other conditions.
[0016] Additional and alternative features, advantages, and
embodiments of the invention are set forth or apparent in the
following detailed description, drawings and claims. Although
numerous implementations and examples of the invention are set
forth--including in this "Summary of the Invention" section--the
examples and implementations are not intended to limit the scope of
the invention as claimed. While the invention was developed during
research on, and is particularly adapted to analysis of sleep
related conditions, it should be readily apparent that the
invention may be used in a number of other applications such as
pre-natal monitoring, determining surgery patient activity,
movement of bedridden individuals and other applications that
skilled artisans would recognize.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The accompanying drawings, which are included to provide a
further understanding of the invention, are incorporated in and
constitute a part of this specification, illustrate embodiments of
the invention and together with the detailed description serve to
explain the principles of the invention. In the drawings:
[0018] FIG. 1 is a flow chart schematically illustrating a first
embodiment of a non-invasive, data acquisition and processing
system constructed according to the principles of the
invention.
[0019] FIG. 2 illustrates an embodiment of a mattress pad layout
containing a suite of sensors that may be employed in the system of
the invention.
[0020] FIG. 3 illustrates a switch profile of the mattress pad
layout described in FIG. 2.
[0021] FIGS. 4A, 4B and 4C schematically illustrate graphical data
indicative of the position of a subject, which may be produced from
a mattress pad layout of the invention.
[0022] FIG. 5 illustrates an embodiment of a pillow layout system
containing a suite of sensors that may be employed in the system of
the invention.
[0023] FIG. 6 illustrates an embodiment of a chair layout
containing a suite of sensors that may be employed in the system of
the invention.
[0024] FIG. 7 is a block diagram illustrating a first embodiment of
a system of the invention for separating respiratory and cardiac
output signals obtained from pressure transducers sensing chest and
abdomen movement.
[0025] FIG. 8 illustrates a first embodiment of the amplifier and
filter circuit constructed according to the principles of the
invention that may be used to process physiological signals
obtained from a vibration sensor.
[0026] FIG. 9 illustrates a second embodiment of an amplifier and
filter circuit constructed according to the principles of the
invention that may be used to process physiological signals
obtained from a vibration sensor.
[0027] FIG. 10 illustrates a third embodiment of an amplifier and
filter circuit constructed according to the principles of the
invention that may be used to process physiological signals
obtained from a vibration sensor.
[0028] FIG. 11 illustrates a micro-controller module circuit
constructed according to the principles of the invention that may
be used to process electronic signals from an amplifier and filter
circuit of the invention.
[0029] FIGS. 12A is an EKG waveform, while FIGS. 12B, 12C and 12D
are sample waveforms, all obtained simultaneously using an EKG
machine, or system for non-invasive analysis of physiological
signals constructed according to the principles of the invention,
respectively.
[0030] FIGS. 13A and 13B are graphs illustrating restlessness
indexes based on information obtained from a non-invasive system
constructed according to the invention.
[0031] FIG. 14 is a block diagram of an algorithm for analyzing the
processed signals according to principles of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0032] The embodiments of the invention and the various features
and advantageous details thereof are explained more fully with
reference to the non-limiting embodiments and examples that are
described and/or illustrated in the accompanying drawings and
detailed in the following description. It should be noted that the
features illustrated in the drawings are not necessarily drawn to
scale, and features of one embodiment may be employed with other
embodiments as the skilled artisan would recognize, even if not
explicitly stated herein. Descriptions of well-known components and
processing techniques may be omitted so as to not unnecessarily
obscure the embodiments of the invention. The examples used herein
are intended merely to facilitate an understanding of ways in which
the invention may be practiced and to further enable those of skill
in the art to practice the embodiments of the invention.
Accordingly, the examples and embodiments herein should not be
construed as limiting the scope of the invention, which is defined
solely by the appended claims and applicable law.
[0033] A method and system are provided whereby multiple types of
sensors may be used to passively detect physiological
characteristics of a subject, such as, for example, physiological
characteristics associated with analyzing quality of sleep.
Preferably, all of the sensors are passive sensors, which operate
without any conscious input from or direct connection to the
subject. The sensors are formed into one or more suites to provide
more complete detection of numerous physiological characteristics.
According to an exemplary embodiment of the invention,
ultra-sensitive vibration sensors are used to provide, through
signal processing techniques, waveforms of heart rate, breathing
rate, snoring and other physiological characteristics. To work in
the sleep environment of the invention, the vibration sensors must
be sensitive enough to detect small movements of the body or body
tissue generated by cardiac forces. More particularly, the voltage
of the sensors may saturate when gross movement occurs, thereby
allowing differentiation between gross movement and cardiac or
respiratory events. These sensors may be piezoelectric, fiber
optic, load cell based or other types of sensors of suitable
sensitivity known in the art, such as piezoelectric air pressure
sensors commercially available from Motorola and others. In concert
with these sensors, temperature sensors, carbon dioxide sensors,
light sensors, electromagnetic sensors and/or simple momentary
contact switches may be provided to form a suite of sensors that
provides multi-dimensional data about the user without the use of
any type of camera or microphone equipment. The piezoelectric
sensor, some of the temperature sensors and the momentary contact
switches may be embedded in a mattress pad, while other sensors may
be embedded in a pillow. Additional sensors may be located proximal
to the bed, such as in a swing arm located above a bed. The sensors
may be made compatible with any existing bed or other support
structure.
[0034] Data from the sensors may be acquired through the use of a
microcontroller/microprocessor module and the use of additional
components, such as instrumentation amplifiers and filters. The
sensors provide longitudinal sleep data (e.g., sleep data collected
over one or more nights) to provide a better understanding of how,
over an extended period of time, a person's sleep can be analyzed
and evaluated to make improvements where necessary. In addition,
this sensor suite may be adapted to fit other applications a
skilled artisan would recognize besides measuring physiological
parameters associated with analyzing quality of sleep such as
monitoring physiological conditions in a chair (including
wheelchairs), monitoring patients that are confined to intensive
care unit hospital beds, monitoring children's vital statistics,
such as those of premature babies, and detecting pressure points
that could possibly develop into bedsores. Additionally, detection
of illness, restless leg syndrome, periodic limb movement and
sleepwalking may also be done. In addition, there may be
applications where monitoring healthy people at rest or in other
states may be beneficial. Various features and exemplary
embodiments of the invention will now be described in more detail
with reference to the figures below.
[0035] FIG. 1 is a flow chart schematically illustrating a first
embodiment of a non-invasive data acquisition and processing system
constructed according to the principles of the invention. While the
data flow is described in a particular manner, it is understood
that the data may flow and be processing in a variety of
configurations, as recognized by skilled artisans. Generally, the
flowchart may be broken into three circuitry sections: a module for
data acquisition 110; a module for arranging data 130; and, a
module for analyzing data 150.
[0036] Inputs from the suite of sensors detecting physiological
conditions of a subject form part of the data acquisition module
110. Data acquisition module 110 includes a mapping switch matrix
112 that provides data to a shift register at 114. Data from the
mapping switch matrix 112 includes data based on the position of
the subject on a mattress, described in more detail below with
reference to FIG. 2, or other support. The shift register 114
outputs the positional data to a micro-controller 128. One or more
temperature sensors 116 provide temperature data to an
analog-to-digital converter 118 that provides the output to
micro-controller 128. By way of example, if the temperature sensor
116 is a digital sensor, the analog-to-digital converter 118 may be
omitted.
[0037] Data acquisition module 110 further includes a vibration pad
120 that provides vibrational and movement data to an
analog-to-digital converter 122, which provides its output to
micro-controller 128. Vibration pad 120 may be an ultra-sensitive
pressure transducer, such as a commercially available piezoelectric
air pressure sensor for sensing respiratory and/or cardiac movement
of a subject. Further, a photo-active sensor 124 provides data to a
circuit 126, (i.e., resistance/capacitance timing circuit), which
communicates with the data micro-controller 128. By way of example,
if the photoactive sensor 124 is a digital sensor, the
analog-to-digital circuit 126 may be omitted. The photo-active
sensor 124 may sense the ambient light to determine the relative
light, the time of day and the conditions of the subject.
[0038] Micro-controller 128 runs a sensor state capture program to
capture the data from the various sensors at some set interval,
with the exception of the vibration pad, and provide a log of that
data. The vibration pad may be sampled continuously for a set
amount of time, such as 5 or 10 seconds. The micro-controller 128
counts the peaks/troughs in the vibration pad signal with set
limits for the maximum possible pulse and respiration rates, and
calculates the pulse and respiration rate based on the count. The
other data is sampled in between the runs of the time interval
(e.g., the 5 or 10 second periods). The capture program involves
sampling the switch matrix 112, the temperature sensor 116, the
vibration pad 120 and the photo-active sensor 124 to see if there
is additional data on a change in state. The data is pulled from
the various sensors. Optionally, the switch matrix 112 could be run
on an additional micro-controller 128 for more sensitive position
and movement analysis.
[0039] A privacy flag 129 sets bits in the data set via a user
operated switch to flag the data for omission from analysis. Thus,
privacy maybe provided for the user when desired.
[0040] The micro-controller 128 provides the interface between the
sensors and the processor, described below. The data acquisition
module 110, in part through micro-controller 128, provides for
addressing and synchronization, as well as adequate sampling of the
signals coming from the sensors, which are converted from analog to
digital format, if necessary.
[0041] In data arrangement module 130, data captured by the
micro-controller is assembled and sent to a processor, such as a
personal computer, at 132. The assembled data may be transmitted by
packets or serially. Other manners of transmitting the data known
in the art may also be used. The processor compares the new data
from each sensor received at 134. If the new data received is the
same as that is in a buffer, the data is discarded and the
processor polls the micro-controller for additional data at 136.
This may minimize the size of the recorded data set.
[0042] If the new sensor data is different from the old, the data
is logged at 138. Logging the data may include writing the data to
a log, recording a time stamp, and/or storing the information in a
buffer. The processor may then poll the micro-controller 128 for
additional data from one or more of the sensors. A log file is
created at 140. The log file may contain information related to one
or more sensors, changes in sensor data, and the time stamp. The
log file may contain all such information since the last
transmission of the log file, as described below. An archiver
assembles the log file for transfer at step 142. According to an
embodiment of the invention, data from the log file may be
transferred at specified intervals (e.g, hourly, daily, weekly,
etc.) or in conjunction with specified events (e.g., heart rate
outside parameters, temperature indicating fever/flushing, etc.). A
server is connected at 144. By way of example, a scheduler may
establish a dial-up connection with the server. The log file is
transferred at 146. The server may comprise a central location for
accessing data obtained by data acquisition module 110.
[0043] In the data analysis module 150, the server receives and
records the log file at 152. The data in the log file may be stored
in a data storage device at 154, according to an embodiment of the
invention. Due to the nature of the data, it may be desirable for
the data to be stored in a secured storage.
[0044] At 156, an analysis routine is performed on the stored data.
A positional analysis is performed at 158, which may include
determining, based on the data, the position of the subject at
various times during the gathering of the initial data. Based on
the positional analysis, a display matrix is interpreted at 160,
i.e., it may be output on a monitor or printed out. By way of
example, the display matrix may be similar to that illustrated in
FIGS. 4A, 4B, and 4C, below.
[0045] In the analysis module 150 information derived from each of
the sensors may be interpreted to provide indications of the state
of various physiological characteristics. These include, but are
not limited to heart rate, HRV, blood pressure, respiratory rate
and regularity, apneas and hypopneas, body surface temperature at
multiple points, restlessness or activity levels, body position, as
well as detailed movement information and pressure points. By way
of example, skin temperature and/or pulse magnitude may be measured
at the extremities, such as the lower leg or foot, and compared to
the measurements of the upper body or torso to calculate a
peripheral vascular resistance (PVR). PVR is a characteristic
linked to the detection of cardiovascular disease. These results
can be displayed through visual feedback, such as a monitor located
remotely or at the apparatus itself, and/or as a summary report or
graph on a personal computer. Thus, the positional analysis at 158
and/or physiological, algorithms pulse rate, restriction, heart
rate variability (HRV) 164 and/or temperature values, etc., may be
integrated into one or more reports at 162. The results may also be
sent through a secure connection to a secure remote database that
is accessible by the primary care provider of the individual or
other necessary health professionals associated with the individual
so as to provide them with this information.
[0046] The analysis routine thus may include a subroutine such as
shown at 164 for determining various physiological characteristics
such as those discussed above. According to one feature of the
invention, one or more physiological algorithms may be used to
determine the pulse or heart rate, the respiration rate, the HRV
rate, and other physiological information. An example of an
algorithm that may be used is discussed below in conjunction with
FIG. 14. The physiological information may also be integrated into
a report or graph at 162. The report may include the parameters on
the subject for studying the physiological information about the
subject. Physiological algorithm 164 analyzes the processed signals
to generate desired waveforms and information.
[0047] FIG. 2 illustrates an embodiment of a mattress pad layout
containing a suite of sensors that may be employed in the invention
to provide some or all of the input to data acquisition module 110.
A mattress 200 may be any type of mattress or other support used by
a person. An array 210 of pressure-sensitive switches 215 are
arranged on the mattress 200 to determine the position of a subject
when lying on the mattress 200. One suitable type of pressure
switch is a pressure-activated binary, which is a transducer that
provides an electrical signal upon a predetermined force or weight
being applied to the sensor. While the term "switches" has been
used, it is understood that various other types of sensors may also
be used to determine the position of the subject, such as light
sensors, and other sensors recognized by a skilled artisan. The
switches 215 may be provided in a single apparatus, such as
arranged in a mattress pad for placing over the mattress 200, or
may be arranged individually on a mattress 200. A large number of
switches 215 in a single apparatus thus may be placed over a
mattress 200 in a relatively quick manner such that the switches
215 are arranged in the array 210 to obtain the desired degree of
sensitivity.
[0048] FIG. 3 illustrates a switch profile that may be obtained
from the mattress pad layout system of the invention, such as
described in FIG. 2. As noted above, an array 210 of switches 215
is arranged on a mattress 200. A subject 300 is shown laying on the
array 210. In this example, the switches activated by the subject
(i.e., that are closed due to the weight of the subject) are shown
as white, while those switches not activated (i.e. that are open)
are shown as dark. The result is a switch profile 310 of the array
210, as shown without the subject 300. As shown, the activated
switches 215 provide an approximate profile of the subject 300. As
discussed above, output from the activated switches 215 is input
into the system at mapping switch matrix 112 to be processed and/or
analyzed to provide information about the position of the
subject.
[0049] As described above, a matrix of momentary contact switches
that are embedded in a mattress pad, block of foam or bed sheet may
be used to provide a positional map of the person lying/sitting
down. This positional map may be used in monitoring sleeping
positions throughout the night, detecting the presence of a person
in a bed or chair or detecting difficulty sleeping.
[0050] In particular, FIGS. 4A, 4B and 4C illustrate data that may
be obtained from various switch profiles that may be produced from
a mattress pad layout of the invention, which are called switch
representations. The switch representations of FIGS. 4A, 4B and 4C
are illustrated with white squares showing data from switches not
occupied by the subject, gray squares showing data from switches
indicating an interpolated point (i.e., some pressure but not
sufficient to close the switch), and dark squares showing data from
closed switches. FIG. 4A illustrates a switch representation where
the subject was laying on his/her stomach. This results in
activated switches to show mostly the torso and the thighs, along
with an arm at the top of the array. FIG. 4B illustrates a switch
representation where the subject was laying on his/her back. This
results in activated switches to show mostly the torso, calves and
feet. FIG. 4C illustrates a switch representation where the subject
was laying on her side. This results in switches activated to show
the torso along with almost the entire length of the legs.
[0051] Returning to the mattress pad layout of FIG. 2, one or more
sensor pads 220 may extend transversely across the width of and are
placed on the mattress 200. As described above, the sensor pads 220
may include ultra-sensitive vibration sensors, such as
piezoelectric, fiber optic, or load cell based sensors that provide
data, which may be input into the system at 120, and sensitive
enough to provide through signal processing techniques, waveforms
of heart rate, HRV, breathing rate, snoring and other physiological
characteristics as described above. The vibration sensor pad 220
may be a foam pad with sensors embedded therein. Alternatively, the
sensor pad 220 may be filled with air. The vibration sensor pads
220 may be placed at chest level (e.g., the rib cage) and abdomen
level (e.g., below the rib cage), to provide readings of signals
for heart rate and breathing. In addition, it may be desirable to
have a vibration sensor pad 220 at leg level (e.g., calves) to read
signals for heart rate.
[0052] The sensor pads 220 are connected to a data storage module
240. The sensors (e.g., switches 215, sensor pads 220, temperature
sensors 230, etc.) and the data storage module 240 may form at
least part of the data acquisition module 110 described above in
FIG. 1. More specifically, data storage module 240 may include
micro-controller 128. The connection may be via wires, or may use a
wireless transmission. The data gathered by the data storage module
240 is then transmitted to a processor 250. Processor 250 may be a
central processor, an Internet server, a personal computer
(including a home personal computer), or any device that allows the
processing of information. Further, the data may be stored in
processor 250, or a module connected to processor 250, such as a
storage device. Transmitting data may include a direct transfer,
such as over a dedicated line, a closed network, the Internet, a
wireless network, a WAN, or the like. Alternatively, transmitting
the data may include storing the data on a readable medium and
physically transferring the readable medium to the processor 250,
where the processor accesses the data from the readable medium.
[0053] FIG. 2 also illustrates temperature sensors 230 which may be
located on the mattress 200. In concert with the array of switches
210 and the vibrating sensor pads 220, the temperature sensors 230
obtain temperature data from the subject, which may be input into
data acquisition module at 116 of FIG. 1.
[0054] In addition, temperature sensors may be embedded in a
mattress pad, block of foam or bed sheet. The sensors may provide
the surface body temperature of a subject that will be used to aid
in the determination of sleep staging. This information may be used
for controlling temperature, such as be activating the heat or the
air conditioner. This information may also be used for activating
white noise, soothing music, or electronically synthesized sound
that would alter its sound profile based on the data obtained from
the patient in order to provide a biofeedback signal to alter
physiologic parameters in a way that might be deemed beneficial.
Alternatively, the information may be used to drive a device that
would touch the patient in various magnitudes and profiles.
[0055] FIG. 5 illustrates an embodiment of a pillow layout system
containing a suite of sensors that may be employed in the
invention. A pillow layout system 500 is located over the mattress
200. The pillow layout system includes a pillow 510, with at least
one light sensor 520 and at least one humidity and/or carbon
dioxide sensor 530. A swing arm 540 having at least one humidity
and/or carbon dioxide sensor 550 may be fixedly or rotatably
mounted to the support frame of the bed supporting mattress
200.
[0056] The light sensor 520 detects light to obtain data for
analyzing ambient light conditions. Data from the light sensor is
input at 124, as described above in FIG. 1. The humidity and/or
carbon dioxide sensor 530 is used to obtain humidity data and
carbon dioxide readings from the subject to obtain data for
analyzing breathing rates, sleep patterns, and other respiratory
information. Data from sensor 530 may be input into the
micro-controller 128 of the data acquisition module 110, although
that input is not specifically shown in FIG. 1.
[0057] Light sensors may quantitatively determine the light level
in a room as part of a logic circuit that would determine if the
person is actually sleeping on the bed or chair. In addition,
humidity sensors may detect which direction a person's breathing is
occurring in as an aid for positional analysis. By way of example,
the humidity analysis information may be used in detecting of sleep
apnea. In addition, carbon dioxide sensors may also be used in
place of or in connection with the humidity sensors. Carbon dioxide
sensors may determine absolute concentration of carbon dioxide in
contact with the sensor, or may be a charge coupled device type
sensor for imaging the carbon dioxide cloud that emerges from the
subject upon exhalation for determining real time lung
function.
[0058] Monitoring temperature and heart rate may be used to
determine the sleep staging of a subject throughout the night as an
assessment of the quality of sleep. The light level monitor may be
used in a logic progression that determines if a person is actually
asleep. Further, the information may be used to determine objective
aspects of the Pittsburgh Sleep Index, as skilled artisans would
recognize.
[0059] As the subject may switch positions on the pillow 510,
thereby potentially covering one or more of the sensors, placing
one or more sensors on the swing arm 540 above the pillow 510
allows data to be obtained. Further, the swing arm 540 may move,
based on the movement of the subject, to allow data to be obtained.
According to one feature, one or more pressure switches (not shown)
in the pillow 510 may be connected to the swing arm 540. The swing
arm may include a motor to extend, retract, or rotate the arm based
on the subject's position.
[0060] The light sensor 520 and the humidity and/or carbon dioxide
sensor 530 may be incorporated within a covering for the pillow
510. Alternatively, the light sensor 520 and the humidity and/or
carbon dioxide sensor 530 may be incorporated directly into the
pillow 510. The swing arm 540 may be attached to a headboard of the
bed. Alternatively, the swing arm 340 may be attached directly to a
wall or to a post forming part of the bed that allows the sensors
to be located above the head of a subject.
[0061] FIG. 6 illustrates an embodiment of a chair layout
containing a suite of sensors that may be employed in a system
constructed according to the principles of the invention. A chair
600 has a vibration sensor pad 610 placed in the back of the chair,
another vibration sensor pad (not shown) in the seat of the chair
and a temperature sensor 620 placed in the seat of the chair 600.
The vibration sensor pad 610 may be identical to vibration sensor
pad 220 described above, but adapted to a chair. Further, a data
storage module 630 may be placed under the chair. Data storage
module 630, along with vibration sensor pad 610 and temperature
sensor 620 may be included in data acquisition module 110 of FIG.
1. Data storage module 630 may correspond to micro-controller 128.
Although not shown, a swing arm or other proximal sensor with one
or more sensors also may be placed above the chair. While only two
sensors 610 and one temperature sensor 620 are shown, it is
understood that a plurality of sensors 610 and temperature sensors
620 may be used. The invention may be incorporated into other
devices besides beds and chairs, such as wheelchairs, couches or
other objects for sitting or lying.
[0062] As described below, electronic components and circuits may
be used to reduce noise and increase gain of the vibration sensor's
transducer. The design, use and combination of filters isolate
specific frequencies so as to divide the signals from the heart
rate and respiration. For example, when used in conjunction with
appropriate sampling rates, respiration and heart rate waveforms
may be obtained. In addition, heart rate variability may be
determined and sleep apnea may be detected.
[0063] FIG. 7 is a block diagram illustrating a first embodiment of
a system for separating respiratory and cardiac output signals of
vibration sensors constructed according to the principles of the
invention. The system 700 receives an input signal 705 from the
subject by way of signals transferred from the sensor pads 710 and
through a pressure transducer 715. The signal is received at an
instrumentation amplifier 720, which may provide adequate buffering
when, for example, a piezoelectric sensor is used and an infinite
common mode rejection ratio (CMRR) is used to reduce sensitivity to
noise. At the output of the instrumentation amplifier the signal
may be split. A first output of the instrumentation amplifier is
provided to a first filter 725, which may be a second order band
pass filter with both cutoff frequencies below about 1 Hz and may
be an active filter. The output of the first filter 725 is
amplified by a non-inverting amplifier 730, and then filtered
through a second filter 735. The second filter 735 may be an
antialiasing filter and a passive filter. The second filter 735
outputs a chest movement output signal 740. From the chest movement
output signal 740, the breathing rate of a subject may be
obtained.
[0064] The second output of amplifier 720 is provided to third
filter 745, which may be a second order low pass filter with a
cutoff frequency below about 1 Hz and an active filter. The output
of the third filter 745 is provided to a fourth filter 750, which
may be a third order high pass filter with the cutoff frequency
below about 50 Hz and an active filter. The output of the fourth
filter 750 amplified by a non-inverting amplifier 755, and then
filtered through a fifth filter 760. The fifth filter 760 may be an
antialiasing filter and a passive filter. The fifth filter 760
outputs a cardiac output signal 765. From the cardiac output signal
765, the heart rate of a subject may be obtained. By way of
example, components 720-735 and 745-760 may be implemented through
digital signal processing.
[0065] FIG. 8 illustrates a first embodiment of the amplifier and
filter circuit constructed according to principles of the invention
that may be used to process physiological signals obtained from a
vibration sensor. A sensor 805 is connected to an instrumentation
amplifier 810. The sensor 805 maybe, for example, a pressure sensor
in a sensor pad 220 used in a mattress layout as shown in FIG. 2,
and whose output is input at 120 in data acquisition module 110
described in FIG. 1. The output signal from the instrumentation
amplifier 810 is fed into a second order band pass filter 820 and
into a second order low pass filter 830. The output signal from the
second order band pass filter 820 is fed into a first non-inverting
amplifier 850. The output signal from the first non-inverting
amplifier 850 is fed into a first low pass, passive filter 860. The
output signal from the first low pass passive filter 860 results in
a waveform corresponding to the chest movement of the subject. By
way of example, the waveform corresponding to the chest movement
may be in a range of between about 0.025 and about 0.515 Hz.
[0066] As described above, the output signal from the
instrumentation amplifier 810 is also fed into a second order low
pass filter 830. The output signal from the second order low pass
filter 830 is fed into a third order high pass filter 840. The
output signal from the third order high pass filter 840 is fed into
a second non-inverting amplifier 855. The output signal from the
second inverting amplifier 855 is fed into a second low pass
passive filter 865. The output signal from the second low pass
passive filter 865 results in a waveform corresponding to the pulse
of the subject. By way of example, the waveform corresponding to
the pulse may be in a range of between about 0.785 and about 18.27
Hz.
[0067] FIG. 9 illustrates a second embodiment of an amplifier and
filter circuit constructed according to principles of the invention
that may be used to process physiological signals obtained from a
vibration sensor. A sensor 905 is connected to an instrumentation
amplifier 910. The sensor 905 may be, for example, a pressure
sensor. The output signal from the instrumentation amplifier 910 is
fed into a second order band pass filter 920 and into a second
order low pass filter 930. The output signal from the second order
band pass filter 920 is fed into a first inverting amplifier 950.
The output signal from the first inverting amplifier 950 has a bias
voltage 960 applied, and-the resulting signal is fed into a first
low pass passive filter 970. The output signal from the first low
pass passive filter-970 results in a waveform corresponding to the
chest movement of the subject. By way of example, the waveform
corresponding to the chest movement may be in a range of between
about 0.025 and about 0.515 Hz.
[0068] As described above, the output signal from the
instrumentation amplifier 910 is also fed into a second order low
pass filter 930. The output signal from the second order low pass
filter 930 is fed into a third order high pass filter 940. The
output signal from the third order high pass filter 940 is fed into
a second inverting amplifier 955. The output signal from the second
inverting amplifier 955 has a bias voltage 965 applied, and the
resulting signal is fed into a second low pass passive filter 975.
The output signal from the second low pass passive filter 975
results in a waveform corresponding to the pulse of the subject. By
way of example, the waveform corresponding to the pulse may be in a
range of between about 0.785 and about 10.25 Hz.
[0069] The circuit 900 has a lower cutoff frequency than the
circuit 800 of FIG. 8. This may reduce or eliminate aliasing due to
the lower sampling rate of the micro-controller, such as the
micro-controller described below. Further, the biasing voltages 970
and 975 may add a DC offset to center the signal in an
analog-to-digital converter's active range detection.
[0070] As described above, the microcontroller/microprocessor
module may acquire the data from all of the sensors and turn it
into a bit stream pattern. The bit stream pattern may then be
logged by a processor into a data file for immediate analysis or
for later review. By way of example, the bit stream may be produced
by the momentary contact switches and fed into the
microcontroller/microprocessor through the use of shift-registers
to provide a positional map of the person on the mattress pad,
block of foam, bed sheet, or other support. This map may be used in
conjunction with the humidity and carbon dioxide sensors to
determine the exact position of the individual or to detect
movements.
[0071] FIG. 10 illustrates a third embodiment of an amplifier and
filter circuit constructed according to principles of the invention
that may be used to process physiological signals obtained from a
vibration sensor. The circuit 1000 may be used within a process and
system of the invention to process physiological signals obtained
from a sensor of the invention such as a vibration sensor shown at
120. An ultra-sensitive vibration (such as a piezoelectric, fiber
optic or load cell based) sensor may be used that, through the
signal processing techniques in the circuit 1000, can provide
waveforms indicative of both heart rate and breathing rate. Circuit
1000 may be provided in an analysis routine, such as in analysis
156.
[0072] In the example of circuit 1000, the raw signal from the
vibration transducer sensor 1005 (which can be replaced with other
types of sensors) is sent through a series of amplifiers and
filters to separate the signal into useable waveforms. At the first
stage 1010, which amplifies the signal, the signal is initially
passed through a 3-op-amp amplifier, which has an infinite common
mode rejection ratio (CMRR). Other types of amplifiers known in the
art may also be used.
[0073] At the second stage 1020, the signal is then passed through
three different filters. The filters in the second stage 1020
include a first filter 1022, a second filter 1024 and a third
filter 1026. The first filter 1022 may be a bandpass filter from
about 0.1 to about 0.8 Hz. The second filter 1024 may be a bandpass
filter from about 0.8 to about 1.5 Hz. The third filter 1026 may be
a bandpass filter from about 1.5 Hz to about 3 Hz. Thus, the signal
is separated into three different channels in the second stage
1020. At the third stage 1030, which amplifies the signals, the
signals are then passed through respective non-inverting amplifiers
1032, 1034 and 1036 that increase the dynamic range and signal to
noise ratio of the signal. These three outputs are then sent
through an analog-to-digital converter (not shown) before being
sent to a micro-controller module as described below in reference
to FIG. 9. This micro-controller corresponds to micro-controller
128 described in FIG. 1 above. An example of such a
micro-controller is the BASIC Stamp 2 module commercially available
from Parallax, Inc.
[0074] Thus, temperature sensors, carbon dioxide sensors, light
sensors, humidity sensors, electromagnetic sensors, and simple
momentary contact switches form a passive sensor suite that
provides multi-dimensional data about the user without the use of
any type of camera or microphone equipment. FIG. 11 illustrates a
micro-controller for receiving the signals from a circuit, such as
circuit 1000 illustrated in FIG. 10. A micro-controller module 1102
is shown having three address lines 1104, 1106, and 1108. In
addition, the micro-controller module 1102 has a load line 1110, a
clock line 1112, a data line 1114, enable lines 1116, 1118 and
1120, and light lines 1122 and 1124.
[0075] To accommodate the large amount of switches being employed
in the circuit 1100, several 8-bit parallel load serial shift
registers 1134 are employed to shift in all of the switches into
just three pins on the micro-controller module 1102. Address lines
1104, 1106 and 1108 are used to address the chips 1126 connected in
parallel to the micro-controller 1102. The enable lines 1116, 1118
and 1120 select the chip 1126 and the address lines 1104, 1106 and
1108 select the input for the selected chip. The outputs of FIG. 8,
9 or 10 are provided in vibration lines 1128, 1130 and 1132.
[0076] FIG. 14 is a block diagram of an algorithm for analyzing the
processed signals according to principles of the invention, such as
shown in the flow chart of FIG. 1 at 164. Data is input at 1402.
The data may be data that has been gathered for any length of time.
For example, the data may have been gathered for 30 seconds or one
minute. The data is then split into multiple arrays at 1404. The
multiple arrays are based on the input channels (e.g., which sensor
the data came from) and the time stamp associated with the data. As
shown, the data is divided into temperature signal(s) 1406, pulse
signals from the upper chest 1408, pulse signals from the lower
chest 1410, respiration signal from upper chest 1412, and
respiration signal from lower chest 1414.
[0077] The voltage measured in the temperature signal(s) 1406 is
converted to the calibrated temperature value 1416. The temperature
value is then sent to output 1458.
[0078] The pulse signals from the upper chest 1408 and the pulse
signals from the lower chest 1410 have recursive bi-directional
filtering performed at 1418. This filtering may smooth the waveform
and subtract data smoothed with a low pass frequency of about 1.5
Hz from data smoothed at a low pass frequency of about 10 Hz. The
respiration signal from upper chest 1412 and the respiration signal
from lower chest 1414 have recursive bi-directional filtering
performed on them at 1420 to create a combined respiratory signal.
This filtering may smooth the waveform and subtract average values
of the dataset from data smoothed with 1.5 Hz filters.
[0079] The peaks and troughs of the filtered signals are detected
with the associated time stamp at 1422. The periods of movement by
the subject and the periods of good signal for the pulse and the
respiration are identified and labeled at 1424. The index numbers
that correspond to the periods of good signal are labeled at 1426.
The index numbers indicate the sample numbers that contain usable
signal data as opposed to movement artifact.
[0080] The respiration signal is analyzed by counting the number of
troughs at 1428. The respiration rate is further analyzed by
determining the phase shift between each of the peaks of the two
respiratory waveforms and labeling any cessation in breathing. The
respiration rate is calculated based on the respiration count at
1430. Sleep apneas may be labeled based on the phase shift. This
information in then sent to output 1458.
[0081] Pulse signal troughs are analyzed to determine the relative
position, sequentially, at 1432. The relative position is indicated
as a "+," a "-" or no change. If a trough is positioned above a
previous one, it receives a "+." If the opposite occurs, the trough
gets a . . . . The signal troughs are examined to find any
instances of a trough marked with a "+" that is preceeded, i.e.,
before by a trough marked with a "-." The time stamp associated
with this trough is compared to the previous entry and the
difference is logged as a heart rate if it falls between the
minimum and maximum range of heart rate values. This is repeated
several times at 1436 with several different limits. For example,
0.3 seconds is marked as the minimum interval (200 beats per
minute), and steps up by increments of 0.05 seconds (e.g., 0.35
seconds, 0.40 seconds, etc.). This produces a list of possible
heart rates given the imposed limits or the algorithm's maximum
allowable heart rate, thereby reducing the chance for larger
errors.
[0082] At 1438, the average, median and range of the instantaneous
heart rate values is determined for each of the limits to create a
list of possible heart rate values representative of the entire
data set. The maximum list range is set to 2.5 beats per minute at
1440. Further, the minimum difference for the "+" and "-" is set at
zero. The list range is compared to the maximum at 1442. If the
list range is under the maximum, the set is declared the final set.
If the list range is not under the maximum, the minimum difference
is incremented by one. Thus, small troughs are increasingly
discarded as noise so as to isolate the true heart rate signals. If
a low count is encountered (e.g., less than two) in either of the
last two heart rate values in the list, if the minimum difference
exceeds 30, or if the overall count for the list is less than
forty-five, the minimum difference is reset to zero. Further, this
increments the list range maximum by 2.5 and then repeats the
process. The algorithm exits this step if the list range maximum
exceeds 75.
[0083] The lists of each heart rate calculation (i.e., upper chest
and abdomen) are compared and analyzed at 1446 based on statistical
measures. The statistical measures include, but are not limited to,
variance, instantaneous pulse count difference, and sequential
difference of heart rates. A representative heart rate value for
each heart rate list (i.e., upper chest and abdomen) is determined
at 1448.
[0084] The best heart rate value for each list is determined and
logged at 1450. Determining the best heart rate value may be based
on the average of the representative values and selecting the one
value with the highest count that does not exceed the possible
count for the data set. The list of instantaneous heart rate values
and their time stamps used to create the selected pulse value in
the list are logged at 1452.
[0085] The heart rate variability index is determined and logged at
1554 using the standard root mean square of sequential differences
(RMSSD) approach. Anomalous beats are corrected based on
statistical measures used to isolate the anomalous beats from
normal beats. Blood pressure is determined at 1456 based on, but
not limited to, the waveform characteristics involving the area
under the curve, the slope of the curve, and the pulse width. This
information is also logged. This information in then sent to output
1458.
[0086] Output 1458 for the entire data set 1460 may comprise
numerous types of information. By way of example only, the
information may include a representative heart rate, a
representative respiratory rate, movement percentage, beat-to-beat
heart rate variability, blood pressure index, obstructive sleep
apnea count, peripheral vascular resistance, and biofeedback
results. Other information may include an instantaneous heart rate,
an instantaneous respiration rate, an instantaneous temperature,
and any cessation of respiration while not moving.
[0087] Using the apparatus and processes described above, numerous
embodiments may be constructed and used to measure physiological
characteristics of a subject. Examples of these embodiments and the
features that may be achieved by these embodiments are described
below. Features of one embodiment may be employed in other
embodiments, even if not explicitly stated herein. According to an
embodiment of the invention, the temperature sensors, carbon
dioxide sensors, light sensors, electromagnetic sensors and the
array of contact switches comprise a suite that provides
multi-dimensional data about the user without the use of any type
of camera or microphone equipment. Various physiological signals
may be obtained to analyze the subject. Further, the data is
preferably obtained completely by non-invasive technology, thereby
allowing monitoring of the subject while reducing or eliminating an
effect on the patient during the act of monitoring. By way of a
specific example, a piezoelectric sensor, four of the temperature
sensors and all the momentary contact switches may be sandwiched
between two foam mattress pads. Four humidity sensors, four carbon
dioxide sensors, two light sensors and potentially one of the
piezoelectric sensors (if two are employed) may be embedded in a
foam pillow. The type of foam used in the pillow may be a foam
known as "memory foam." The actual mattress pad may be a sandwich
of memory foam and high resilience foam. It may also employ a
rubber sheet in between the two types of foam to provide necessary
surface contact for the switches. This would make it compatible
with any existing bed and allow the user to use standard sheets and
pillowcases. The remaining two sensors, one carbon dioxide and one
humidity sensor could be mounted on a swing arm that would be more
ably attached to the headboard by conventional means. This arm may
swing out over the person's head when the person is deemed asleep.
As described above, one or more pressure switches in a pillow
and/or the mattress may indicate the position of the subject,
thereby allowing the swing arm to move as appropriate. Anytime the
person lifts their head up the arm will retract so the person does
not hit their head on the arm. If necessary, a fan may be employed
to induce the surrounding airflow over the carbon dioxide and
humidity sensors, which need to be calibrated against the ambient
background levels.
[0088] According to another embodiment of the invention, an
apparatus and method for the passive monitoring of physiological
parameters such that sensors work together to acquire a
multidimensional data set that can be interpreted to provide health
diagnostic data or general health assessments includes various
components. These components may include a vibration sensor for the
sensitive detection of physiological characteristics including
heart rate and breathing rate. The vibration sensor, in combination
with the amplifiers and filters described above, may provide
breathing and heart rate waveforms in a clear enough manner for an
algorithm to interpret the waveforms and automatically provide
heart rate and breathing rate information. For instance, heart rate
waveform data can be interpreted to provide a measure of heart rate
variability. An electromagnetic sensor may provide electromagnetic
information about a subject. Also, by way of example, the process
and apparatus may be used for detecting sleep apnea by examining
the breathing waveform that flat-line during a period of absence of
respiration or multiple waveforms that show a pattern of
paradoxical.
[0089] In addition, multiple instances of monitors that would
obtain blood flow through various parts of the body (i.e., from
chest to foot, neck to hand, etc.) may be used. The sensors would
record pulse data and the time delay or phase shift between the two
signals, thus providing the flow data or a measure that correlates
with peripheral vascular resistance. By way of example, the process
and apparatus may be used for obtaining a cuff-less blood pressure
measurement interpreted from the flow rate data or from
characteristics of the pulse waveforms such as area under the
curve, rate of rise and change in magnitude of the contractile
signals, rate of fall and change in magnitude of the dilation
signal, ratio of contractile to dilation signal, and or pulse
width. By way of an alternative example, the process and apparatus
may be used in a mattress pad, block of foam or bed sheet that
could be laid on top of a bed to provide pulse and breathing data
during sleep. Alternatively, the process and apparatus may be used
in a chair to monitor pulse and breathing rates during waking hours
while the patient is sitting in a chair.
[0090] One application for which the invention as described above
is particularly suitable is passive sleep monitoring. Having
multiple piezoelectric sensor-pad combinations, one at the upper
chest level and one at the abdomen level, provides a means to
obtain multiple waveforms. The respiratory waveforms are generally
out of phase during normal breathing and become in-phase during
paradoxical breathing experienced when obstructive sleep apneas
occur. Results have been obtained with the invention within five
percent of accepted measurements of a pulse oximeter. Cardiac
information is also pertinent to sleep architecture and quality.
The system of the invention is able to examine the heart rate on a
beat-by-beat basis, providing measures of heart rate, heart rate
variability and blood pressure, all of which are key
characteristics during sleep. Additionally, restlessness data from
the movement artifact provides a general indication of sleep
quality and comfort. The entire basic dataset (cardiac,
respiratory, thermal and movement) provide a means of acquiring
sleep staging, at the very least on a REM, non-REM level. This
would be done through the use of analyzing all of these variables
in lieu of attaching electrodes to monitor the person's brain waves
through an electroencephalogram (EEG). Thus, the system of the
invention looks at the results from the actions implemented by the
brain rather than the actual brain waves themselves. Monitoring all
of these parameters related to sleep in a longitudinal fashion,
i.e., over a number of sleep cycles and/or days, can lead to a
clearer picture of a person's sleep hygiene and architecture,
including the effects on the quality of sleep and quantification of
sleep quality. The system may be used in the subject's home, thus
reducing a subject's discomfort. In addition, the system is capable
of determining efficacy of treatment, since it is designed to
monitor longitudinally.
[0091] FIGS. 12B, 12C and 12D are sample waveforms obtained from a
system for non-invasive analysis of physiological signals
constructed according to the principles of the invention. A
vibration pad sensor, such as vibration pad sensors 220 in FIG. 2,
was placed at chest level on a subject and another vibration pad
sensor was placed at abdomen level of the subject. The vibration
pad sensors were foam pads with tubing at one end and a
piezoelectric sensor at the other end of the tubing. The data from
the vibration pad sensor was processed through a circuit similar to
circuit 800 in FIG. 8.
[0092] FIG. 12A is an EKG waveform taken of the subject during the
same time of the measurements that resulted in the sample waveforms
of FIGS. 12B, 12C and 12D of the invention. FIG. 12B is a sample
upper chest pulse waveform. FIG. 12C is a sample abdomen pulse
waveform. The chest and abdomen pulse waveforms are compared to
each other in an algorithm, such as that described in FIG. 14, so
that if for some reason there is noise in one set, the other can be
used. If both waveforms are good, they are compared to provide a
more accurate assessment of heart rate. Ultimately, the best
waveform is selected out of numerous processing techniques, so it
provides added redundancy and reliability to enhance the accuracy
and precision of the sensor. The .degree.`3rd of 10 pictured"
systolic peak in FIGS. 12A, 1213, 12C refers to the fact that all
three heart rate waveforms are displaying the same data set,
meaning that the labeled peak is the same heartbeat in each of the
figures, i.e., the waveforms represent the same data. Thus, the
heart rate waveforms of the invention have comparable accuracy to
that of conventional EKG readings.
[0093] FIG. 12D is a sample respiration waveform. The respiration
waveform illustrates both breathing based on chest movement 1210
and breathing based on abdomen movement 1220. As can be seen by the
example in FIG. 12D, the peak of the chest movement waveform 1210
is offset from the peak of the abdomen movement waveform 1220.
[0094] FIGS. 13A and 13B are graphs illustrating restlessness
indexes based on information obtained from a non-invasive system
constructed according to the invention. This data was taken using
an embodiment of the invention using the circuit in FIG. 10 and
taken from two different nights of sleep behavior of the subject.
FIGS. 13A and 13B arc bar graphs illustrating the number of
movement sensor firings on the vertical axis and the time of day on
the horizontal axis. FIG. 13A illustrates a relatively restless
night's sleep, as there are a number of time periods with
relatively large numbers of movement sensor firings. FIG. 13B
illustrates a relatively restful night's sleep, as there are
relatively few time periods with movement sensor firings. Further,
there are time periods when no movement sensors fired.
[0095] Additionally, the movement artifact associated with the
piezoelectric sensor provides a gauge of restlessness or activity
while in contact with the sensors. Since the sensor is amplified to
detect minute movement associated with cardiac events, gross body
movements can be easily differentiated from cardiac or respiratory
events, as the sensor voltage normally saturates, or clips, when
movement occurs.
[0096] Graphs such as those shown in FIGS. 12B-12D, 13A and 13B may
be used in combination to analyze the sleeping patterns of an
individual. More specifically, heart rate, pulse waveforms,
respiratory rate and restlessness information may provide
information to allow the analysis of a subjects sleep patterns.
Other analyses based on the information may also be performed.
[0097] Another application of the invention involves use in
hospital beds. For patients that do not require close monitoring,
the system may obtain vital signs and physiological characteristics
without the need for the patient to have any instrumentation
attached to his/her body. This would maximize the comfort of the
patient while minimizing the intrusiveness. Providing this extra
information could help caregivers maintain a well-informed view of
patient's status or even alert them when they might be coming out
of anesthesia. Since there is a documented fall risk for patients
in hospitals, the passive monitoring of the invention provides
automated alerts for patient activity so caregivers can be aware
when patient's attempt to get up or move around. This way, they can
provide assistance when needed before an accident may occur. One
skilled in the art could see how this might apply to other
situations in the hospital such as during or after hemodialysis
treatments or other treatments that affect the cardiac or
respiratory systems, or other environments.
[0098] Yet another application applies to bedridden individuals.
Pressure ulcers are caused when a patient lies in one position for
an extended period of time without being moved or shifting
position. The system could provide vital information as to how much
and what part of a patient has moved over a period of time. It
could also indicate if there are particular pressure points that
might be susceptible to the development of pressure bedsores. This
would remove the burden of tracking how often a patient has moved
off of the caregivers while automating and quantifying the
process.
[0099] The disclosures of the following U.S. Patents and
publications are incorporated by reference herein in their
entirety: U.S. Pat. No. 6,342,039; U.S. Pat. No. 6,222,064; U.S.
Pat. No. 5,891,023; U.S. Pat. No. 5,590,650; U.S. Pat. No.
4,163,447; U.S. Pat. No. 5,902,250; U.S. Pat. No. 5,335,657, U.S.
Pat. No. 5,295,490; U.S. Pat. No. 4,306,657; Harada, T., Sato, T.,
Mori, T. "Estimation of Bed-Ridden Human's Gross and Slight
Movement Based on Pressure Sensors Distribution Bed," The
University of Tokyo; Alihanka, J., Vaahtoranta, K., Saarikivi, I.,
"A New Long-Term Monitoring of Ballistocardiogram, Heart Rate and
Respiration," J. Physiol., Vol. 240, pp. 384-392, 1981; and Tamura,
T., Togawa, T., Murata, M., "A Bed Temperature Monitoring System
for Assessing Body Movement During Sleep," Clin. Phys., Physiol.
Meas., 1988, Vol. 9, No. 2, pp. 139-45.
[0100] While the invention has been described in terms of exemplary
embodiments, those skilled in the art will recognize that the
invention can be practiced with modifications and in the spirit and
scope of the appended claims. These examples given above are merely
illustrative and are not meant to be an exhaustive list of all
possible designs, embodiments, applications or modifications of the
invention.
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