U.S. patent application number 17/314144 was filed with the patent office on 2021-10-21 for micro motion detection for determining at least one vital sign of a subject.
This patent application is currently assigned to Linet spol s.r.o.. The applicant listed for this patent is Linet spol s.r.o.. Invention is credited to Ondrej Bradac, Vladimir Kolar, Petr Seba.
Application Number | 20210321882 17/314144 |
Document ID | / |
Family ID | 1000005751521 |
Filed Date | 2021-10-21 |
United States Patent
Application |
20210321882 |
Kind Code |
A1 |
Seba; Petr ; et al. |
October 21, 2021 |
Micro Motion Detection for Determining at least one Vital Sign of a
Subject
Abstract
A system and method for determining at least one vital sign of a
subject comprises a plurality of pressure sensors configured to be
placed in a vicinity of the subject's body, and configured and
operable to sense movements of skin of the subject's body within at
least one region on the skin and generate sensing data
corresponding to the at least one region; the sensing data
comprising a plurality of measured signals being indicative of a
common physiological event differentiated in time and intensity
from one another and a control unit in data communication with each
of pressure sensors of the plurality of pressure sensors; the
control unit comprising an analyzer processing utility configured
and operable to receive the sensing data corresponding to each of
the at least one region; generate, for the pressure sensors
associated with each of the at least one region, a pressure
variation profile for the region; identify therein one or more
predetermined signatures indicative of at least one physiological
event of the subject; generate signature data thereof; extract at
least one time stamp from the signature data; and generate vital
sign data indicative of at least one vital sign of the subject
based thereon.
Inventors: |
Seba; Petr; (Bartosovice v
Orlickych horach, CZ) ; Kolar; Vladimir; (Slany,
CZ) ; Bradac; Ondrej; (Praha 5, CZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Linet spol s.r.o. |
Slany |
|
CZ |
|
|
Assignee: |
Linet spol s.r.o.
Slany
CZ
|
Family ID: |
1000005751521 |
Appl. No.: |
17/314144 |
Filed: |
May 7, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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17291916 |
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PCT/IL2019/051295 |
Nov 27, 2019 |
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17314144 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/6823 20130101;
A61B 5/6814 20130101; A61B 2562/0247 20130101; A61B 5/0245
20130101; A61B 5/031 20130101 |
International
Class: |
A61B 5/0245 20060101
A61B005/0245; A61B 5/00 20060101 A61B005/00; A61B 5/03 20060101
A61B005/03 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 2, 2018 |
IL |
263409 |
Claims
1. A method for determining vital signs of a subject, the method
comprising: sensing movements of the skin of one or more regions of
the subject and generating sensing data for each region; analyzing
the sensing data and generating a variation profile for sensing
data of each region; identifying, in each of the variation
profiles, one or more predetermined signatures indicative of at
least one physiological event of the subject; extracting time
stamps of the one or more identified predetermined signatures; and
generating vital sign data indicative of at least one vital sign of
the subject based on the extracted time stamps.
2. The method of claim 1, wherein generating vital sign data
comprises determining time relation between different time
stamps.
3. The method of claim 2, wherein the relation between different
time stamps comprises a difference between time stamps.
4. The method of claim 1, wherein sensing comprises sensing from
two or more sub-regions in each region.
5. The method of claim 4, wherein the variation profile is a
curvature of an n-dimensional curve, wherein `h` is the number of
sub-regions in each region.
6. The method of claim 5, wherein the predetermined signature is
characterized by a threshold of at least one projection of the
curve.
7. The method of claim 1, wherein the sensing data comprises
intensities of pressure samples sensed in each region over
time.
8. The method of claim 1, wherein at least one region comprises the
head of the subject.
9. The method of claim 1, wherein at least one region comprises the
chest of the subject and at least one region comprises the abdomen
of the subject.
10. The method of claim 1, comprising receiving electrical signal
data of the subject; extracting time stamps of physiological events
from the electrical signal data; and generating vital sign data
indicative of at least one vital sign of the subject based on a
relation between time stamps extracted from the electrical signal
data and time stamps extracted from the signatures of the pressure
variation profile.
11. The method of claim 10, wherein the electrical signal data
comprises an ECG signal.
Description
CROSS-REFERENCE TO RELATES APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 17/291,916, filed May 7, 2021, which is a
United States National Stage Application of International
Application No. PCT/IL2019/051295, filed Nov. 27, 2019, which
claims priority to IL Application No. 263409, filed on Dec. 12,
2018, the disclosures of which are incorporated herein by
reference.
BACKGROUND OF THE INVENTION
[0002] The present disclosure is in the field of vital signs and
physiological parameters detection, specifically by non-invasive
measurement of skin motions.
[0003] Vital signs and various physiological parameters are crucial
in monitoring the state of a patient. Many of these physiological
parameters are measured by measurement units that are clipped onto,
attached, or introduced invasively into the patient. When a patient
is undergoing long treatment, these measurements can be bothersome,
or even may cause harm to the patient. Measuring these vital signs
by non-invasive measurement that is taken without the patient being
aware of such, may improve conditions for the patient during the
period of treatment in a hospital or health center.
SUMMARY OF THE INVENTION
[0004] The invention relates to a system and method for
identifying, in a non-invasive manner, internal
phenomena/physiological events of a subject, by sampling pressure
applied on the external surface of the subject, namely the skin,
and determining, based thereon, at least one vital sign. The vital
sign can be any physiological parameter, such as heart rate, blood
pressure, an organ's activity, intracranial pressure, blood
velocity in a predefined path, etc.
[0005] In order to determine the vital signs, one or more pressure
sensors are placed to sense skin movements of a sensing region of
the body skin, whose movements are correlated with a physiological
event, such as specific heart activity (e.g., the opening and/or
closing of the aortic valve). The pressure sensors are configured
to sense movements on a micron-size scale. The pressure sensors can
sense the movements indirectly, namely via a layer of a mattress or
pillow.
[0006] From each sensing region, measurements of the pressure over
time, by the sensors sensing the region, are used to generate
sensing data. The sensing data is analyzed to generate a pressure
variation profile which corresponds to the development of the
pressure measurements of a region in time. Predetermined signatures
are then recognized in the pressure variation profile, which are
expressions of the physiological event, and the time stamps thereof
are extracted. The time stamps and/or relations between time stamps
are indicative of vital signs of the subject. The present technique
uses at least two sensors placed at different locations to be able
to obtain a vector force process in place which is not accessible
easily, for example inside the cranial region. The sensors are thus
placed in proximity to different parts of the body surface. Inner
force processes in the body causes pressure changes on the body
surface which are then measured by the sensors. In such a way, data
obtained by the sensors are synchronous signatures of the true
force processes occurring in the body. The predetermined signatures
have the same transformation properties as the inner force
processes (i.e., it is invariant under the same transformation
group). The sensors create a coordinate system into which the
forces are projected. The force vector can be reconstructed when
the force projection is measured in three independent directions.
The plurality of sensors enables to obtain a sensing data forming a
geometrical object (i.e., the signal curve (.beta..sub.1(t),
.beta..sub.2(t), . . . .beta..sub.n(t). As soon as the curve
changes directions in the n-dimensional space, this can be observed
in the corresponding curvature.
[0007] In some instances, time stamps from two or more sensing
regions are extracted, and a relation between time stamps of
different sensing regions is correlative to a vital sign of the
subject. For example, time difference between a time stamp
correlated with the passage of the blood through the aortic arch,
and a time stamp correlated with a pulse wave reflection of the
aortic bifurcation, is indicative of a pulse wave propagation
velocity.
[0008] Therefore, an aspect of the present disclosure provides a
system for determining at least one vital sign of a subject. The
system includes a plurality of pressure sensors configured to be
placed in a vicinity of the subject's body. The sensors can be
placed such that they sense movement of the skin indirectly, namely
via mediator substances, such as cloths, polymeric players,
fabric-based substances etc. In some other embodiments, the sensors
can be in direct contact with the skin of the subject to sense
directly the movements thereof.
[0009] The sensors are configured and operable to sense movements
of the skin of the subject's body within one or more regions of the
skin of the subject and generate sensing data corresponding to the
at least one region. A region can be associated with a single or a
plurality of sensors such that each of the sensors is configured to
sense a common physiological event. The physiological event may be
for example, bifurcation of the aorta into the common iliac
arteries, opening of the aortic valve, the pulse propagation
through the aortic arch, contraction of the heart atria, etc. The
sensing data comprises a plurality of measured signals being
indicative of a common physiological event differentiated in time
and intensity from one another. For example, sensors that are
configured to sense the propagation of blood along the aortic arch,
are disposed at different locations associated with different
locations of the aortic arch. As a result of this configuration,
the pressure measurements that are expressed on the skin and
associated with blood propagation in the aortic arch, differ in
time and intensity between different sensors.
[0010] Sensing data is generated for each region based on the
measurements obtained by the sensors associated with the region,
the sensing data comprising a collection of pressure measurements
from the sensors associated with the region over time.
[0011] The system includes a control unit that is in data
communication with each of the plurality of pressure sensors to
receive the sensing data, each correlated with a specific
region.
[0012] The control unit includes an analyzer processing utility
configured to analyze the sensing data corresponding to each of the
regions, and generate, based thereon, a pressure variation profile
for each region. The analyzer processing utility identifies in the
pressure variation profile one or more recognizable signatures.
These signatures are correlated with an internal physiological
event and a signature data is generated based on the identified
signatures. The analyzer processing utility, or an extractor
module, is configured to extract and generate vital sign data from
the signature data indicative of at least one vital sign of the
subject. It should be noted that the step of extraction may be
performed by the analyzer processing utility, or by another module
being in data communication with the analyzer processing
utility.
[0013] In some embodiments, the extractor module is configured to
extract time stamps corresponding to the signature data and
generate the vital sign data based thereon.
[0014] In some embodiments of the system, the pressure sensors are
arranged in a plurality of arrays. The plurality of arrays may
include first and second arrays, associated with first and second
regions of the skin of the subject. The pressure variation profile
is generated for each region based on the sensing data received
from the sensors associated with the region. The relations between
time stamps may be for example the time difference/delay between
linked physiological events in different arrays of sensors. In this
connection, it should be understood that the number of arrays used
in the technique of the present invention depends on the vital sign
to be determined. For example, for measuring the moments of atria
contraction, isovolumetric heart muscle contraction and/or aortic
valve opening, one array is sufficient. For measuring pulse wave
velocity, two arrays are needed.
[0015] The first and second regions may be associated with
different physiological events and sensing data is generated with
respect to each region, namely a first sensing data corresponding
to the first region, and a second sensing data corresponding to the
second region.
[0016] In some embodiments of the system, the control unit is
configured and operable to analyze and extract first and second
time stamps corresponding to the first and second regions. The time
stamps may be related to one another by a consecutive physiological
event, namely an event that initiates on a first location that is
associated with the first region and continues to a second location
that is associated with the second region. The control unit
analyzes and extracts a relation between the first and second time
stamps and determines a vital sign based on the relation. The vital
sign may include a physiological condition referring to a
physiological parameter or physiological state that is derived by
the technique of the present disclosure and is not necessarily
measured directly by the pressure sensors. For example, a
physiological condition may be derived from an indirect measure,
(e.g., by calculation based on direct measurements of two
physiological events).
[0017] In some embodiments, the relation comprises time difference
between the first and second time stamp. For example, identifying a
time difference between the passage of blood through the aortic
arch, and the passage of the blood in the aortic bifurcation, is
indicative of the pulse wave propagation.
[0018] Pressure sensors associated with the same region may be
located in sub-regions arranged in a spaced-apart relationship
within the region. Thus, sensors associated with a common region
may vary by their pressure measurements of a common physiological
event in time and intensity.
[0019] In some embodiments, a layer is disposed between the
subject's skin and the sensors such that measurement is performed
indirectly. The layer may be part of a mattress or a pillow.
[0020] In some embodiments of the system, at least some of the
sensors are in direct contact with the skin of the subject.
[0021] The pressure sensors are configured to sense micron-sized
movements of the skin. In some embodiments, the sensors comprise a
piezoelectric component. The sensors may further comprise a
capacitor component. The piezoelectric component is configured to
sense relatively fast signals, and the capacitor component is
configured to sense relatively slow signals. In other words, the
sensor has a fast sampling rate component configured as a
piezoelectric transducer and a slow sampling rate component
configured as a capacitor. Examples of such sensors are described
in PCT applications with publication Nos. WO 2015/051770, WO
2017/220055 and WO 2017/220056.
[0022] In this connection, it should be noted that, in the present
disclosure, signals obtained from the plurality of sensors can be
related to projections of a certain signal curve to arbitrary axis.
This curve can be further described by its Euclidean invariants
(i.e., the Cartan curvatures (or by the corresponding affine
curvatures)). Generally, a system of `n` sensors leads to an
n-dimensional curve, which is invariantly described by n-1
curvatures (one dimension is reduced). In some embodiments, for
further evaluation of the physiological events, only the first
Cartan curvature is used. The inventor has found that higher
curvatures contain higher derivatives and are hence more polluted
by the noise.
[0023] In some embodiments, the control unit is configured and
operable to filter out (disregard) signals having a poor signal to
noise ratio, such as high curvatures.
[0024] In some embodiments, the control unit is configured and
operable to process the sensing signals by using a monitoring
function being an affine or Euclidian transformation to improve the
signal to noise ratio, and to identify personal signatures.
[0025] Therefore, in some embodiments of the system, the variation
profile, generated based on the sensing data of each region, may be
a curvature of an n-dimensional curve, wherein `n` is the number of
pressure sensors that are associated with the corresponding region.
In other words, measurements of each sensor of the region,
correspond to a one-dimensional projection of the n-dimensional
signal curve. For example, in the instance of 3 sensors sensing a
common region, the signal curve is 3-dimensional. The n-dimensional
signal curve may be further described by its geometric invariants
called n-dimensional curvatures.
[0026] Predetermined signatures of the n-dimensional curvature may
be correlated to a rate of change in at least one projection of the
signal curve. For example, if the curvature exceeds a predetermined
threshold, the analyzer processing utility identifies a signature
and the time stamp of the signature is extracted. In some
embodiments, the predetermined signature is characterized by a
threshold of at least one projection of the signal curve.
[0027] In some embodiments of the system, at least one region is
associated with one of the head, abdomen, or chest of the subject.
In some embodiments a first region is associated with one of the
head, abdomen, or chest of the subject, and a second region is
associated with an organ other than that associated with the first
region.
[0028] In some embodiments of the system, the system includes an
input module being in data communication with each of the pressure
sensors of the plurality of pressure sensors to receive the sensing
data corresponding to each of said at least one region. The input
module may be also configured to receive biologic-electrical signal
data of the subject, the biologic-electrical signal data comprising
electrical signatures of physiological events. The extractor module
is configured to extract one or more time stamps of the electrical
signatures of physiological events from the electrical signal data.
Vital sign data is generated based on a relation between time
stamps extracted from the electrical signal data, and time stamps
extracted from the signatures of the pressure variation
profile.
[0029] The biologic-electrical signal data may comprise an
electrocardiogram (ECG) signal. The ECG signal may provide a time
indication of heart activity and the relation between a specific
heart activity, and a time stamp derived from a sensing region may
be indicative of a vital sign. For example, such an ECG signal may
be used in order to identify the initiation of a pulse cycle. It
should be noted that if the system is coupled to an ECG device, one
array of a plurality of sensors is sufficient to determine at least
one vital sign.
[0030] In some embodiments, the system further comprises an ECG
measurement device configured to provide the biologic-electrical
signal data. The sensing signals may be measured simultaneously
together with an ECG signal.
[0031] In some embodiments, the system measures simultaneously an
ECG signal and the signal obtained from all the mechanical sensors
with a sampling rate of 1 kHz.
[0032] In a specific and non-limiting example, the following steps
are performed:
[0033] The maximum of R wave in the ECG is localized (this is the
electric trigger that starts contraction of the heart
chambers);
[0034] The signal from the mechanical sensors starts at the time of
the R wave maximum, and ends 1/2 second after it is acquired;
[0035] The curvature is calculated from this signal;
[0036] The curvature maxima is found from this signal;
[0037] The next R wave maximum is found, and steps 2-5 are
repeated.
[0038] The curvature maxima related to the cardiac cycle appear
with the same (or similar) time delay with respect to the
corresponding R wave. Other maxima, that are not related to the
heart cycle and originate for instance from swallowing, fluctuation
and similar, are ignored. Each ECG related curvature maximum
appears with some time delay after the R wave and this delay
remains stable (it changes only slowly).
[0039] With sensors placed below the torso, the following can be
identified: [0040] 1. Aortal valve opening: the related time delay
give information about the heart muscle contractility (i.e., how
quick the left chamber contracts to create blood pressure that is
able to open the aortal valve). [0041] 2. The pulse propagation
through the aortal arch and diaphragm. This time depends on the
aortal stiffness and the thorax pressure. [0042] 3. Reflection of
the pulse on the abdominal aortal branching into the iliac arteries
depending on the aortal stiffness. [0043] 4. Aortal valve closing.
[0044] 5. A maxima can be also related to blood pulse in the
kidneys, which are visible only in some patients.
[0045] All of this can be measured continuously, whenever the
subject does not move. The reactions of the cardiovascular system
on administrated medicaments can be observed on-line. Clinically
relevant information (for instance the pulse wave velocity along
the aorta) can be obtained as well.
[0046] With sensors placed below the head, several curvature peaks
related to the heart cycle and corresponding to the pulse
reflection inside the skull can be identified. The exact reflection
time depends on pressure conditions inside the skull and enables to
monitor intracranial pressure changes non-invasively.
[0047] Another aspect of the present disclosure concerns a method
for determining vital signs of a subject. The method includes
sensing movements of the skin of one or more regions of the
subject, each region being associated with a different
physiological event that is expressed on the skin of the subject.
The sensing data comprise intensity of pressure measurements over
time. For each region, generating sensing data is based on one or
more pressure measurements performed in the region. The method
further includes analyzing the sensing data and generating a
variation profile for the sensing data of each region, and
identifying, in each of the pressure variation profiles, one or
more signatures indicative of at least one physiological event of
the subject. Time stamps are extracted from the identified
predetermined signatures, and vital sign data indicative of at
least one vital sign of the subject, is generated, based on the
extracted time stamps.
[0048] In some embodiments of the method, a time relation between
different time stamps is determined, the time difference being
indicative of the vital sign of the subject. The time relation may
be a time difference between time stamps.
[0049] Each region may be formed by sub-regions, and the step of
sensing movements of the skin comprises sensing for two or more
sub-regions.
[0050] In some embodiments of the method, the variation profile is
a curvature of an n-dimensional curve, wherein `n` is the number of
sub-regions in each region, namely the number of pressure sensors
associated with the same region. In other words, all measurements
of sensors of the same array are fused to generate an n-dimensional
curvature, which can be referred as an "array sensing data"
(namely, the Euclidean invariants).
[0051] In some embodiments of the method, the pressure variation
profile is a curvature of an n-dimensional curvature, wherein `n`
is the number of sub-regions being sensed in the region. The
predetermined signature may be characterized by a threshold of a
curvature degree, namely a degree of a rate of change in at least
one projection of the signal curve.
[0052] In some embodiments, the n-dimensional curvature enables to
reduce the influence of synchronal vibrations coming from the
floor. It should be understood that the force changes caused by a
resting human body are small, especially when they are measured by
sensors placed below the bed mattress. Vibrations coming from the
floor are usually larger. They appear when somebody walks alongside
the bed, a door closes, and similar. These vibrations come from the
bottom and are transmitted to the sensors from the underside of the
bed. This is however a rigid structure that moves as one whole. The
signals obtained by different sensors from the underside are
therefore similar. If they are identical, the related signal curve
will be just a straight line, and its curvature will be equal to
zero.
[0053] In some embodiments of the method, at least one region is
associated with a portion of the head, abdomen, or chest of the
subject. In some embodiments at least a first region is associated
with one of the head, abdomen or chest of the subject, and a second
region is associated with an organ different to that of the first
region.
[0054] In some embodiments, the method comprises receiving
biologic-electrical signal data of the subject. The
biologic-electrical signal data comprises electrical signals
derived from electrical measurements of the subject. Time stamps of
physiological events from the biologic-electrical signal data are
extracted and vital sign data is generated based on a relation
between time stamps extracted from the biologic-electrical signal
data and time stamps extracted from the signatures identified in
the pressure variation profile.
[0055] The biologic-electrical signal data may comprise data of an
ECG signal.
BRIEF DESCRIPTION OF THE DRAWINGS
[0056] In order to better understand the subject matter that is
disclosed herein and to exemplify how it may be carried out in
practice, embodiments will now be described, by way of non-limiting
example only, with reference to the accompanying drawings, in
which:
[0057] FIGS. 1A-1C are block diagrams of different embodiments of
the system according to the present disclosure.
[0058] FIG. 2 is an example of data presentation received from the
measurement of six pressure sensors.
[0059] FIG. 3 is a schematic illustration of an example of a sensor
array for sensing different regions of a patient that lies
horizontally.
[0060] FIG. 4 is a schematic illustration of the aorta that
demonstrates different portions that are measured by the
non-invasive system and method of the present disclosure.
[0061] FIG. 5 is a chart of an example of presentation of the
pressure variation profile having a curvature variation profile
that is generated based on measurement of the pressure sensors.
[0062] FIG. 6 is a schematic illustration of a sensor array in a
head-pressure measuring unit.
[0063] FIGS. 7A-7B are examples of presentations of the pressure
variation profile having a curvature variation profile that is
generated based on sensing data of 4 sensors that sensed micro
motions from the head of a subject. FIG. 7B shows a marking of the
identified signatures from FIG. 7A as compared with morphology in
the invasively measured intracranial pressure pulse waves.
[0064] FIG. 8 is a chart of comparative data between non-invasive
measurement of the present disclosure, and conventional invasive
measurement of intracranial pressure.
[0065] FIG. 9 is a flow diagram of a method for determining a vital
sign of a subject according to the present disclosure.
DETAILED DESCRIPTION OF THE EMBODIMENT
[0066] A system according to the present disclosure is exemplified
in FIG. 1A, which is a schematic block diagram of the system. The
system 100 for determining a vital sign of a subject includes a
plurality of pressure sensors 104A, 104 B . . . configured to be
placed in a vicinity of the subject's body and configured and
operable to sense movements of skin of the subject's body within at
least one region on the skin. Although in the figure, for sake of
illustration only, two pressure sensors 104A, 104 B are shown, the
invention is not limited to such a configuration, and any desired
number of pressure sensors may be used. Measurements from different
pressure sensors related to the same sensing region are correlated
to a common physiologic event such as propagation of blood through
the aortic arch or the opening of the aortic valve. Each pressure
sensor is configured to sense pressure from a different sub-region,
which constitutes a portion of the sensing region. The pressure
sensors 104A, 104B are configured to sense micron-sized movements
of the skin. The sensors 104A, 104B may comprise a piezoelectric
component. The sensors 104A, 104B may further comprise a capacitor
component. The piezoelectric component is configured to sense
relatively fast signals, and the capacitor component is configured
to sense relatively slow signals. Each sensor has a fast sampling
rate component configured as a piezoelectric transducer and a slow
sampling rate component configured as a capacitor. Examples of such
sensors are described in PCT applications with publication Nos. WO
2015/051770, WO 2017/220055 and WO 2017/220056.
[0067] Pressure sensors 104A and 104B generate a sensing data
SD.sub.i, which is the collection of pressure measurements from all
pressure sensors associated with a common sensing region. System
100 comprises a control unit 106 in communication with the pressure
sensors 104A and 104B and is configured and operable for receiving
and analyzing the sensing data to generate vital sign data
indicative of at least one vital sign of the subject. The control
unit 106 is configured generally as a computing/electronic utility
including inter alia such utilities as data input and output
modules/utilities 106A and 106B, memory 106D (i.e., non-volatile
computer readable medium), and analyzer/data processing utility
106C. The utilities of the control unit 106 may thus be implemented
by suitable circuitry and/or by software and/or hardware components
including computer readable code configured for implementing the
operations of method 900 shown in FIG. 9 and described below.
[0068] The features of the present invention may comprise a
general-purpose or special-purpose computer system including
various computer hardware components, which are discussed in
greater detail below. Features within the scope of the present
invention also include computer-readable media for carrying or
having computer-executable instructions, computer-readable
instructions, or data structures stored thereon. Such
computer-readable media may be any available media, which are
accessible by a general-purpose or special-purpose computer system.
By way of example, without limitation, such computer-readable media
can comprise physical storage media such as RAM, ROM, EPROM, flash
disk, CD-ROM or other optical disk storage, magnetic disk storage
or other magnetic storage devices, or any other media which can be
used to carry or store desired program code means in the form of
computer-executable instructions, computer-readable instructions,
or data structures and which may be accessed by a general-purpose
or special-purpose computer system. Computer-readable media may
include a computer program or computer application downloadable to
the computer system over a network, such as a wide area network
(WAN) (e.g., Internet).
[0069] In this description and in the following claims, a "control
unit" is defined as one or more software modules, one or more
hardware modules, or combinations thereof, which work together to
perform operations on electronic data. For example, the definition
of a processing utility includes the hardware components of a
personal computer, as well as software modules, such as the
operating system of a personal computer. The physical layout of the
modules is not relevant. A computer system may include one or more
computers coupled via a computer network. Likewise, a computer
system may include a single physical device where internal modules
(such as a memory and processor) work together to perform
operations on electronic data. While any computer system may be
mobile, the term "mobile computer system" or the term "mobile
computer device" as used herein, especially include laptop
computers, netbook computers, cellular telephones, smartphones,
wireless telephones, personal digital assistants, portable
computers with touch sensitive screens, and the like. Control unit
106 may be comprised of a processor embedded therein running a
computer program or attached thereto. The computer program product
may be embodied in one or more computer readable medium(s) having
computer readable program code embodied thereon. The computer
readable medium may be a computer readable signal medium or a
computer readable storage medium. Computer program code for
carrying out operations for aspects of the present invention may be
written in any combination of one or more programming languages.
The program code may execute entirely on the user's computer,
partly on the user's computer, as a stand-alone software package,
partly on the user's computer and partly on a remote computer, or
entirely on the remote computer or server. In the latter scenario,
the remote computer may be connected to the user's computer through
any type of network, including a local area network (LAN) or a wide
area network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). These computer program instructions may be
provided to the processor of a general-purpose computer, special
purpose computer, or other programmable data processing apparatus
to produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
The specified functions of the processor can be implemented by
special purpose hardware-based systems that perform the specified
functions or acts, or combinations of special purpose hardware and
computer instructions.
[0070] The control unit 106 or the optional data input module 106A,
if any, may include a communication module for receiving the
sensing signal data SD.sub.i. The sensing data SD; of each array
may thus be communicated to the input module 106A or directly to
the control unit 106. The analyzer processing utility 106C receives
the sensing data SD; from the data input module 106A and analyzes
it to generate a pressure variation profile for each region being
associated with each pressure sensor. The pressure variation
profile is generally a profile indicative of difference between
pressures which originated from different sensors of a common
region. The variation profile is indicative of internal
physiological events or phenomena and their expression on the skin
surface, constituting together the signal curve, which may be
manifested by its Euclidian invariants by a series of functions
such as Cartan's curvatures. In some embodiments, by using
invariants of group theory (Euclidean, Affine) a time stamp or
pattern may be extracted from the signature. The time stamp or
pattern may be defined as Cartan and/or affine curvatures which
correspond to the pattern of the body force process that is
occurring directly inside the body, and for which a direct
measurement is not possible. Memory 106D is configured for storing
a learning database (i.e., preselected data indicative of profiles
of the pressure variation profile correlated with an internal
physiological event). The database may be implemented with
Microsoft Access, Cybase, Oracle, or other suitable commercial
database systems. Memory 106D and may be relayed via wireless or
wired connection by an external unit to a central database. The
processing utility 106C may record the received sensing signal data
SD; in a learning database in memory 106C and/or may
query/cross-reference the received sensing signal data SD; with
data in the learning database to identify signatures Sig; in the
pressure variation profile. To this end, the preselected data
stored in the learning database may be used to compare the
signatures Sig; in the pressure variation profile with the
signatures of an internal physiological event stored in the
learning database. The signatures Sig; are indicative of at least
one physiological event of the subject. The processing utility 106C
is thus configured to identify in the pressure variation profile
for each region, one or more predetermined signatures, and generate
signature data thereof. The processing utility 106C or an extractor
module 112 is then configured to extract time stamps corresponding
to the signature data, and process them to determine at least one
vital sign of the subject and generate vital sign data indicative
of at least one vital sign of the subject based thereon. Time
stamps of the predetermined signatures refer to a certain
identifiable time-dependent profile of the signature data. The
learning database includes also preselected data indicative of time
stamps of the predetermined signatures correlated with at least one
vital sign. This last step may be performed by the processing
utility 106C or by an extractor module 106E receiving from the
processing utility 106C the signatures Sig; and being capable for
extracting time stamps of the signatures and determining at least
one vital sign of the subject upon analysis of time stamps of the
signatures. The at least one vital sign of the subject may then be
outputted by the optional data output module 106B.
[0071] FIGS. 1B-1C are block diagrams of other embodiments of the
system according to the present disclosure. The system 110 of FIG.
1B includes one or more pressure sensors arrays 102. For sake of
simplicity, one pressure sensors array 102 is shown in the figure.
However, the invention is not limited to one pressure sensor array,
and any desired number of pressure sensor arrays may be used. Each
pressure array 102 includes a plurality of pressure sensors. Each
array 102 is associated with a sensing region, which is a location
on the surface skin of the subject. The system 110 1B may be
connected to an electrocardiogram (ECG) unit 114 that is configured
to measure electrocardiogram signals of the subject under
inspection, generate electrocardiogram data (ECG) of the subject,
and communicate it to the input module 106A. The sensing data SD;
may be measured simultaneously together with the ECG data. The
electrocardiogram data ECG is analyzed by the analyzer processing
utility 106C and predetermined electrocardiogram signatures EDS;
are identified.
[0072] More specifically, in this case, the learning database of
memory 106D comprises preselected data indicative of
electrocardiogram signatures EDS; correlated with internal
physiological parameters and/or specific physiological events. The
preselected data is used to compare the signatures EDS; with the
preselected signatures stored in the learning database and to
correlate between identified signatures and specific physiological
events according to their relation to the electric-cardiogram
signatures EDS.sub.i. Alternatively, and additionally, extractor
module 106E may extract electrocardiogram time stamps therefrom to
find a relation between them and the time stamps extracted from the
identified signatures Sig. By finding the relation between the time
stamps of the two measurements (i.e., pressure-based and electrical
based measurement), internal physiological parameter or at least
one vital sign of the subject can be determined. FIG. 1C shows
another embodiment of the system 120 of the present disclosure. The
system 130 differs from that of FIG. 1A in that it receives
measured data MD from an external measurement unit 116 such as an
ECG unit, pulse measurement device etc. The external measurement
unit 116 generates the measurement data MD and transmits it to the
input module 106A. The external measured data MD is analyzed by the
analyzer processing utility 106C and measurement data signatures
MDS are identified. Time stamps thereof are then extracted from
measurement data signatures MDS by the extractor module 106E so as
to generate, based thereon, vital sign data VSD. The vital sign
data VSD comprises data related to at least one vital sign or
physiological parameter of the subject and it may be transmitted to
an external notification unit 118, such as a monitor or an audio
speaker, via the data output module 106B to output the vital sign
data to a user.
[0073] As described above, each sensing region is sensed by an
array of a plurality of sensors being configured to generate
sensing data that can undergo further analysis to derive the vital
sign of the subject. Each sensor of the array is configured to
sense from a different sub-region that is comprised within the
sensing region. FIG. 2 shows a graph illustrating an example of an
array of six sensors sensing from six sub-regions of a common
sensing region. The graph presents the normalized voltage of the
pressure sensed by each sensor over time, wherein the voltage of
the n.sup.th sensor has been shifted by 5 multiplied by n for
visualization convenience. This is an example of sensing data SD;
that is generated by the sensors and communicated to the control
unit for further analysis.
[0074] In the figures throughout the application, like elements of
different figures were given similar reference numerals shifted by
the number of hundreds corresponding to the number of the figures.
For example, element 304 in FIG. 3 serves the same function as
element 104 in FIGS. 1A-1C.
[0075] FIG. 3 is a schematic illustration of a top view of a
subject with indications of regions and sub-regions being sensed on
the subject's skin. In this specific and non-limiting example, the
subject 320 lies on a patient's bed that accommodates two arrays of
sensors, each of the arrays having three pressure sensors. The
plurality of pressure sensors is arranged in an optimized
arrangement, in order to monitor various biological/physiological
parameters. In some embodiments, this technique may utilize a
preliminary stage of selection of the sensing signals of certain
measurement sessions performed by a desired group/array of the
plurality of pressure sensors and also selection of the
predetermined time intervals. The processing of the measured
signals from the multiple sensors may also utilize comparison
between the signals measured by different sensors arranged with a
known distance between them.
[0076] The pressure sensors can sense movements of a micron-size
scale directly (e.g., upon physical contact with the skin of
subject) or indirectly, namely via a layer (not shown) of a
mattress or pillow. A layer (not shown) can thus be disposed
between the subject's skin and the sensors. The layer may be a
portion of a mattress or a pillow. In the example, the first array
is configured to sense a portion of the chest region 322 of the
subject. Each of the sensors 304A, 304B, 304C is configured to
sense a corresponding sub-region 324A, 324B, 324C. The second array
is configured to sense a portion of the abdomen region 326. Each of
the sensors 304D, 304E, 304F is configured to sense a corresponding
sub-region 328D, 328E, 328F. Therefore, sensors 304A, 304B, 304C
generate a first sensing data and sensors 304D, 304E, 304F generate
a second sensing data. The first sensing data, derived from the
chest region 322, comprises data indicative of physiological events
which occur in the chest and its vicinity. For example, the first
sensing data may comprise data relating to the opening of the
aortic valve and blood passage through the aortic arch. Each of the
sensors 304A, 304B, 304C measures pressure on the skin surface of
the subject 320, differentiated in time and intensities from the
other sensors. However, it should be understood that the sensing
data from each sensor is correlated to the same internal
physiological events. This is exemplified in FIG. 4, which is an
illustration of the aorta, including the aortic arch. These
measurements constitute data for generating the pressure variation
profile in regions 422 and 426. Sensors 304A, 304B, 304C of FIG. 3
generate sensing data indicative of physiological events occurring
in the chest, and sensors 304D, 304E, 304F generate sensing data
indicative of physiological events in the abdomen and its vicinity.
For example, a physiological event in the abdomen may be
bifurcation of the aorta into the common iliac arteries. In the
same manner, sensors 304D, 304E, 304F measure pressure on the skin
surface of the subject 320, differentiated in time and intensities
from the other sensors, but correlated to the same internal
physiological events.
[0077] An example of a presentation of the pressure variation
profile is shown in a chart in FIG. 5. FIG. 5 shows the pressure
variation profile as generated by the control unit as a function of
time in milliseconds. The obtained data are synchronized with
respect to the R-wave of the corresponding ECG signal that is
localized at 100 ms. The bright areas represent a value of the
first Cartan curvature of the pressure variation profile. The
brighter the area, the greater the curvature. The dark areas
represent times with low pressure variation curvature. As can be
seen, there are gray and white areas that have a constant time
delay with respect to the R-wave of the corresponding ECG signal (R
wave is localized at 100 ms). They represent curvature changes
related to heart muscle contraction and blood ejection (note that
the R-wave serves as a trigger for heart contraction). There are
also areas characterized by alternating and noisy brightness, which
represent a pressure variation profile that is not related to the
triggering R-wave and is dominated by background noise or subject
motion. In this specific and non-limiting example, the left side of
the chart is the pressure variation profile generated based on a
first sensing data, derived from a sensing region associated with
the chest of a subject, and the right side of the chart is the
pressure variation profile generated based on a second sensing
data, derived from a sensing region associated with the abdomen of
a subject. The areas of interest are identified, 530 on the left
side of the chart, and 532 on the right side, and the time of these
areas of interest is determined. The time of each of the areas of
interest can be determined based on an average of a plurality of
measurement cycles or based on a single measurement cycle. These
areas of interest are representations of internal physiological
events and are indicative of a physiological condition or parameter
of the subject. In this example, area 530 represents two
physiological events, the opening of the aorta, which is
represented by the top bright line in area 530, and the pulse
propagation through the aortic arch, which is represented by the
bottom bright line in area 530. Area 532 represents a reflection of
bifurcation of the blood in the abdomen.
[0078] The time difference between the times of the physiological
events, namely the time difference DT between the time stamps
thereof, are indicative of a vital sign of the subject. In this
example, the time difference between the propagation of the pulse
through the aortic arch and the bifurcation of the blood in the
abdomen is indicative of the pulse wave propagation velocity. By
knowing the distance the blood travels between the aortic arch and
the bifurcation in the abdomen, the wave propagation velocity can
be calculated. The distance may be obtained according to some
parameters of the subject, such as age, gender, etc., which are
known from the literature.
[0079] This kind of measurement can be taken continuously while a
patient lies on a patient's bed without the need to physically
connect the patient to any measurement device. The sensors can be
embedded within the patient's bed, (e.g., in the mattress or below
the mattress), and sense the micro-movements of the patient, as
long as the patient lies on the bed and generally does not
move.
[0080] FIG. 6 is a schematic illustration of an array of sensors
which can be accommodated in a head measuring unit such as a
pillow. As can be appreciated, sensors 604A and 604B are arranged
at one side of the pillow 640, and sensors 604C and 604D are
arranged on the other side. This configuration allows to measure
sufficient data from micro-movements of the skin surface of the
head to determine data indicative of the intracranial pressure of
the subject.
[0081] The measurement of vital signs from micro-movements of the
head is similar to the speed pulse rate detection. From the
measured motion of the head, the Cartan invariants are counted,
namely the pressure variation profile is generated, and the
corresponding events and signatures, are found. To determine the
value of intracranial pressure, the time lag/difference between
these events and an ECG R-wave of QRS complex of the ECG is
extracted and calculated.
[0082] Without being bounded to theory, the following is an example
of finding the relation between the time lag/difference between the
events detected in the head and the ECG R-wave.
[0083] If the Moens-Korteweg equation is translated into arterial
pressure, the relation V.about.a {square root over (P)}, is
obtained, where a is a constant, V is the pulse velocity and P is
the arterial pressure.
[0084] By determining the pulse wave propagation velocity, the
corresponding arterial pressure can be calculated. This is commonly
used in pressure estimation by pulse wave pulse measurement.
However, this relationship is also related to noninvasive
measurement of intracranial pressure. In the cranial cavity, the
arteries are embedded in a non-zero pressure environment. From the
point of view of the artery, this means that pressure on its wall
comprises arterial pressure, from which the intracranial pressure
is related, according to the following equation:
CPP=MAP-ICP
where CPP is arterial wall pressure (perfusion pressure), MAP is
mean arterial pressure and ICP is mean intracranial pressure.
According to the above, this may lead to the assumption:
.DELTA. .times. .times. T .about. 1 v .about. 1 MAP - ICP .about. a
.function. ( ICP - MAP ) , ##EQU00001##
where a is a constant. In order to determine the MAP, the arterial
pressure may be measured, preferably on the patient's hands.
[0085] Delay time .DELTA.T is the time difference between the ECG
R-wave and the moment of the event detected in inversions from the
head being measured. If intracranial pressure increases, the time
difference increases, and vice versa. In fact, there are several
events in the head that essentially duplicate the morphology of the
invasively measured intracranial pulse.
[0086] Intracranial pulse-induced cardiac activity usually contains
3 maximas known as P1, P2 and P3 (ICP pulse morphology).
[0087] The inventors found that the data of the invasively measured
ICP wave morphology corresponds to mechanical phenomena in the
head, which subsequently manifests itself as events in the
corresponding invariant. This is demonstrated in FIGS. 7A-7B
illustrating cardiac stroke cycles vs. time in milliseconds. FIG.
7A is a mechanical invariant, counted with 4 headrest sensors (as
illustrated in FIG. 6 above) for time intervals that start with
R-wave ECG and end with 600 milliseconds (time-lock). In this
connection, it should be noted that the evaluation of ICP can be
done with an array of two sensors placed under the patient's head.
An array of four mechanical sensors can also be used as shown in
FIG. 6, configured to provide complex head motions from more
directions, and therefore better resolution. The individual
invariants, namely the measurement cycles, are arranged next to
each other so that consecutive heart strokes are presented along
the X-axis, while the corresponding calculated invariant is
displayed on the Y-axis. The result is a two-dimensional map on
which signatures of events (maximum invariants) are displayed. In
other words, a bright colored area represents a relatively high
rate of curvature change, and a dark colored area represents a
relatively low rate of curvature change. There are total of 11568
consecutive cardiac strokes shown in FIGS. 7A-7B.
[0088] The extracted time stamps of the identified signatures of
events are shown in FIG. 7B together with the invasively measured
intracranial pressure. The identified signatures are marked in the
figure as the lines corresponding to P1, P2 and P3. As can be
appreciated, the events represent the morphology of the invasively
measured pulse wave, including their changes over time
(corresponding to the maxima of P1, P2, P3).
[0089] FIG. 8 shows comparative data between the present
disclosure's non-invasive measurement, and an invasive measurement
of ICP of a patient. The figure presents data of consecutive 8-day
measurement, in which the dark grey areas represent the present
disclosure's non-invasive measurements, and the light grey areas
the invasive measurements. The non-invasive method correctly
follows long-term trends in intracranial pressure changes. Local
mismatches are mostly driven by patient movements on the bed, which
also lead to inaccuracies in invasive measurement.
[0090] FIG. 9 is a flow diagram of an example of a method for
carrying out the invention of the present disclosure for
determining a vital sign of a subject. The method includes
receiving sensing data step 940 of pressure measurements from one
or more regions of the skin surface of a subject. The sensing data
is analyzed step 942 to generate a pressure variation profile step
944 of the measurements. Predetermined signatures are identified
step 946 in the pressure variation profile, and the predetermined
signatures are indicative of internal physiological events that are
expressed on the skin surface of the subject. Time stamps of the
identified predetermined signatures are extracted step 948 and
based on the extracted time stamps, data indicative of at least one
vital sign is generated step 950. The data may be generated based
on a single time stamp or any relation between two or more time
stamps of the identified predetermined signatures.
[0091] In accordance with the provisions of the patent statutes,
the principle and mode of operation of this invention have been
explained and illustrated in its preferred embodiment. However, it
must be understood that this invention may be practiced otherwise
than as specifically explained and illustrated without departing
from its spirit or scope.
* * * * *