U.S. patent application number 16/866224 was filed with the patent office on 2020-08-20 for method and apparatus for processing a cyclic physiological signal.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Haris DURIC, Steven Antonie Willem FOKKENROOD, Geert Guy Georges MORREN, Jens MUEHLSTEFF, Bin YIN.
Application Number | 20200260996 16/866224 |
Document ID | 20200260996 / US20200260996 |
Family ID | 1000004811206 |
Filed Date | 2020-08-20 |
Patent Application | download [pdf] |
United States Patent
Application |
20200260996 |
Kind Code |
A1 |
YIN; Bin ; et al. |
August 20, 2020 |
Method and apparatus for processing a cyclic physiological
signal
Abstract
The invention relates to a method and apparatus for processing a
cyclic physiological signal (30, 40, 52, 53, 54). The method
comprises the steps of repeatedly collecting (2) the physiological
signal (30, 40, 52, 53, 54) over a time period (31, 32, 33)
covering two or more cycles of the cyclic physiological signal (30,
40, 52, 53, 54), wherein a next time period (31, 32, 33) is
adjacent to or overlaps with a previous time period (31, 32, 33),
extracting values (3, 13) of a set of predefined parameters from
the physiological signal (30, 40, 52, 53, 54) within each time
period (31, 32, 33) which parameter values characterize the
physiological signal (30, 40, 52, 53, 54) within the time period
(31, 32, 33), and classifying (4, 14) the physiological signal (30,
40, 52, 53, 54) within each time period (31, 32, 33) based upon the
extracted set of predefined parameter values. This provides for an
efficient analysis of a cyclic physiological signal which is
especially suitable for continuous monitoring of patients where a
trend of a reliable physiological signal is more important than an
instantaneous measurement of a reliable physiological signal, such
as in a general ward environment of a hospital and/or in a home
environment.
Inventors: |
YIN; Bin; (Shanghai, CN)
; DURIC; Haris; (Bothell, WA) ; MORREN; Geert Guy
Georges; (Vissenaken, BE) ; FOKKENROOD; Steven
Antonie Willem; ('s-Hertogenbosch, NL) ; MUEHLSTEFF;
Jens; (Aachen, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
Eindhoven |
|
NL |
|
|
Family ID: |
1000004811206 |
Appl. No.: |
16/866224 |
Filed: |
May 4, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13576687 |
Aug 2, 2012 |
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PCT/IB2011/050510 |
Feb 7, 2011 |
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16866224 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/7221 20130101;
A61B 5/0255 20130101; A61B 5/113 20130101; A61B 2562/0219 20130101;
A61B 5/7264 20130101; A61B 5/0816 20130101; A61B 5/721 20130101;
A61B 5/7275 20130101; A61B 5/0205 20130101; A61B 5/024
20130101 |
International
Class: |
A61B 5/113 20060101
A61B005/113; A61B 5/00 20060101 A61B005/00; A61B 5/08 20060101
A61B005/08; A61B 5/0255 20060101 A61B005/0255; A61B 5/0205 20060101
A61B005/0205; A61B 5/024 20060101 A61B005/024 |
Foreign Application Data
Date |
Code |
Application Number |
Feb 12, 2010 |
EP |
10153489.9 |
Claims
1. A method of processing a cyclic physiological signal, the method
comprising the steps of: repeatedly collecting the physiological
signal over a time period covering two or more cycles of the cyclic
physiological signal, wherein a next time period is adjacent to or
overlaps with a previous time period; extracting values of a set of
predefined parameters from the physiological signal within each
time period which parameter values characterize the physiological
signal within the time period; and classifying the physiological
signal within each time period based upon the extracted set of
predefined parameter values.
2. The method as defined in claim 1, further including a step of
extracting the frequency of the classified physiological signal for
each time period, after the step of classifying the physiological
signal.
3. The method as defined in claim 2, further including a step of
calculating a confidence value for the extracted frequency of the
physiological signal within each time period after the step of
extracting the frequency and based upon the values of the set of
predefined parameters extracted from the physiological signal in
the corresponding time period and the extracted frequency
value.
4. The method as defined in claim 1, wherein the step of
classifying comprises labeling each time period as accept or as
reject.
5. The method as defined in claims 2 and 4, wherein the frequency
of the physiological signal is not extracted for each time period
that is labeled as reject.
6. The method as defined in claim 1, wherein the physiological
signal represents a respiration and/or pulse of a patient.
7. The method as defined in claim 1, wherein the predefined
parameters characterize the physiological signal as a function of
time, frequency and/or position in space.
8. The method as defined in claim 1, wherein in the case of an
overlap between the next and previous time period, the step of
classifying is performed only for the part of the next time period
that does not overlap with the previous time period.
9. The method as defined in claim 1, wherein the step of
classifying is also based upon a statistical database of the set of
predefined parameters.
10. The method as defined in claim 1, further including a step of
conditioning the physiological signal before the step of repeatedly
collecting the physiological signal, wherein the step of repeatedly
collecting the physiological signal comprises repeatedly collecting
the conditioned signal.
11. The method as defined in claim 1, wherein multiple
physiological signals are collected simultaneously and wherein the
steps of extracting and classifying are performed for each of the
multiple physiological signals separately.
12. An apparatus for monitoring a cyclic physiological signal, the
apparatus comprising: a sensor adapted for measuring the cyclic
physiological signal; a time period definition unit adapted for
repeatedly defining a time period covering one or more cycles of
the cyclic physiological signal in which time period the
physiological signal is analyzed, wherein a next time period is
adjacent to or overlaps with a previous time period; an extraction
unit adapted for extracting values of a set of predefined
parameters from the physiological signal within the time period
which parameters characterize the physiological signal within the
time period; and a classifying unit adapted for classifying the
physiological signal within the time period based upon the
extracted set of predefined parameter values.
13. The apparatus as defined in claim 12, wherein the sensor
comprises a multi-axial accelerometer and wherein the measured
physiological signal comprises multiple sub-signals corresponding
to the multiple axes and which sub-signals are analyzed
synchronously in time.
14. The apparatus as defined in claim 12, further comprising a
frequency determination unit for determining the value of the
frequency of the classified physiological signal.
15. The apparatus as defined in claim 12, wherein the apparatus
comprises multiple sensors and is suitable for measuring multiple
physiological signals each of which is analyzed either separately
or in combination.
Description
CROSS-REFERENCE TO PRIOR APPLICATIONS
[0001] This application is a Continuation application of U.S.
National Phase application under 35 U.S.C. .sctn. 371, Ser. No.
13/576,687, filed on Aug. 2, 2012, which claims the benefit of
International Application Serial No. PCT/IB2011/050510, filed on
Feb. 7, 2011, which claims the benefit of European Application No.
10153489.9, filed on Feb. 12, 2010. These applications are hereby
incorporated by reference herein.
FIELD OF THE INVENTION
[0002] The invention relates to a method and apparatus for
processing a cyclic physiological signal.
BACKGROUND OF THE INVENTION
[0003] The monitoring of vital body signs of patients, such as
respiration rate and heart rate, in an intensive care environment
in a hospital has requirements different from those in monitoring a
patient in a general ward setting or in a home environment. The
intensive care unit of a hospital requires an instantaneous and
high reliability of the monitored parameters, whereas the general
ward environment of a hospital is more focused on the trend of the
monitored parameters.
[0004] As an example, respiration rate has proven to be a good
indicator of the deterioration of the condition of a patient and it
plays a crucial role in early warning hospital systems in
combination with other vital body signs. Therefore, a need for
continuous and reliable monitoring of a respiration signal is seen
especially in the intensive care units of hospitals. A similar
need, with less stringent requirements on the reliability and the
instantaneous presentation of the monitored parameters, is present
in the general ward settings of hospitals and in home healthcare
applications, such as in telemedicine and chronic disease
management. While continuous monitoring of the respiration signal,
from which the respiration rate is extracted, is available on
bedside monitors for intensive care patients, various portable
sensor systems are being developed to allow unobtrusive and
prolonged measurement and monitoring of the respiration signal of
mobile patients in general wards with minimal discomfort.
[0005] Motion artifact is a well known issue in patient monitoring
as a whole, which refers to a contamination of the physiological
signal and a degradation of the measurement quality caused by
physical activities of a patient, such as posture change, movement
and talking. Especially for cyclic physiological signals it can
become very difficult to extract the right frequency from the
contaminated cyclic signal. The motion artifact issue is more
pronounced in a general ward setting than in an intensive care unit
setting, since patients in the general ward setting generally have
a more mobile activity pattern and are monitored most of the time
without constant nursing surveillance, thus lacking knowledge on
the presence of physical activities and the measurement context.
The problem becomes even more severe in the monitoring of patients
in home healthcare settings.
[0006] To allow the reliable use of such continuous monitoring
systems, the issue of motion artifacts must be tackled. Most of the
reported studies focus on various signal retrieval schemes, where
often adaptive noise cancellation is applied for cleaning up the
motion-contaminated signal. There are a couple of intrinsic
difficulties in these schemes that are hard to overcome. For
example, motion artifacts may be induced by multiple noise sources
that are difficult to identify and estimate. Another drawback is
that these schemes usually require a large computational effort and
are thus not efficient for a portable system.
[0007] U.S. Pat. No. 5,546,952 discloses a method and device for
determining the validity of a respiratory waveform from a signal
having non-respiratory artifacts, including monitoring a
respiratory effort waveform for a parameter of the waveform that is
characteristic of a non-respiratory artifact. The parameter is
compared with a predetermined limit to determine whether a valid
respiratory waveform has been detected. This is useful in
obstructive sleep apnea treatment where, in case a valid
respiratory effort waveform is available, electrical stimulation of
a patient is then validly limited to the inspiratory phase of the
respiratory cycle, and, in case a valid respiratory effort is not
detected, electrical stimulation is suppressed. The selected
parameters of the waveform that are monitored can be, for example,
inspiratory rise time, inspiratory time-to-peak, time of
inspiratory onset to expiratory offset, inspiratory peak-to-peak
time, expiratory peak-to-peak time or breath-to-breath time.
Initialization of the respiratory signal analysis process occurs
when the system is turned on or reset in which the system tracks
several respiratory cycles to set an amplifier gain and to
establish the normal morphological parameters of the waveform. A
time reference is established with respect to the last inspiratory
onset so that a predicted onset can be calculated for the next
breath which is used to synchronize the electrical stimulation with
the respiratory cycle in case of a valid respiratory effort
waveform. Although this method does not apply adaptive noise
cancellation, it still is aimed at providing a reliable
instantaneous respiration signal on a peak-by-peak basis, which is
important in intensive care settings, but which is not a
prerequisite in a general ward setting.
SUMMARY OF THE INVENTION
[0008] It is an object of the present invention to provide an
efficient analysis of a cyclic physiological signal which is
especially suitable for continuous monitoring of patients where a
trend of a reliable physiological signal is more important than an
instantaneous measurement of a reliable physiological signal on a
peak-by-peak basis, such as in a general ward environment of a
hospital and/or in a home environment.
[0009] In a first aspect of the present invention a method of
processing a cyclic physiological signal is provided, the method
comprising the steps of: [0010] repeatedly collecting the
physiological signal over a time period covering a two or more
cycles of the cyclic physiological signal, wherein a next time
period is adjacent to or overlaps with a previous time period;
[0011] extracting values for a set of predefined parameters from
the physiological signal within each time period which parameters
characterize the physiological signal within the time period; and
[0012] classifying the physiological signal within each time period
based upon the extracted set of predefined parameter values.
[0013] The method according to the invention thus provides a
classification of different time periods or frames of a cyclic
physiological signal based on a set of characteristic parameters or
features the values of which are extracted only for that specific
time period or segment of the physiological signal. There is no
instantaneous signal analysis, such as for example on a peak-by
peak basis, but rather a time frame, or time segment, of the
periodic, or cyclic, physiological signal is analyzed, wherein the
time segment spans two or more cycles. By repeating the analysis of
the physiological signal for each of the different time periods,
the total physiological signal will be segmented or divided up into
several time periods each being classified separately. Thus, each
selected time period or segment of the physiological signal is
characterized by a specific classification, which can be used, for
example, for indicating to what extent the physiological signal is
contaminated by, for example, motion artifacts. So, instead of
retrieving information out of contaminated measurement results, a
signal analysis is employed which automatically identifies and
classifies signal segments, for example as a quality measure which
indicates how well each signal segment represents the physiological
measurement. The signal analysis runs in a sliding time window
mode. The time window or time period, covering at least a couple of
cycles, slides over the signal trace and the signal segment within
each time window or period is analyzed by extracting values for the
set of predefined parameters and by classifying the signal segment
within each time window based on these extracted parameter values.
The method provides for an improved way of trending a physiological
signal resulting in a reliable and classified physiological signal
thus providing a replacement of the computationally intensive
instantaneous representation of an artifact-free physiological
signal.
[0014] In an embodiment of the method according to the invention,
the method further includes a step of extracting the frequency of
the classified physiological signal for each time period, after the
step of classifying the physiological signal. The calculation of
the frequency or rate of the cyclic physiological signal is
performed based on the classified physiological signal, thus
ensuring a reliable input for the extraction of frequency of the
physiological signal. The frequency of the physiological signal for
example comprises a respiration rate or a heart rate, which can be
defined as the number of breaths per minute or the number of heart
beats per minutes, respectively.
[0015] In a further embodiment, the method further includes a step
of calculating a confidence value for the extracted frequency of
the physiological signal within each time period after the step of
extracting the frequency and based upon the values of the set of
predefined parameters extracted from the physiological signal in
the corresponding time period and the extracted frequency value. In
certain circumstances, not only the frequency of the physiological
signal but also knowledge about the confidence of the calculated
frequency is important. This information provides useful input in
further processing of the frequency data, for example for the
proper selection of a frequency or rate to report and appropriate
frequency or rate trend analysis.
[0016] In an embodiment of the method according to the invention,
the step of classifying comprises labeling each time period as
accept or as reject. In this way it is decided for each time period
of the physiological signal whether the physiological signal in
that time period is acceptable, or in other words resembles a
normal physiological signal without any significant contamination,
or whether the physiological signal in that time period is not
acceptable, for example as a result of motion artifacts. This
provides for a processed physiological signal with time periods
that are classified as not acceptable or bad, which time periods
then may be ignored in the further processing of the physiological
signal. On the other hand, the time periods of the processed
physiological signal that are classified as acceptable or good, can
be considered to be a reliable representation of the physiological
signal without any significant disturbance and thus can be used as
a reliable input for further processing of the signal.
[0017] In a preferred embodiment, the frequency of the
physiological signal is not extracted for each time period that is
labeled as reject. In this way it is prevented that a time period
which contains a contaminated physiological signal is used to
calculate an erroneous frequency of the cyclic physiological
signal. Only the signal segments identified as good or acceptable
will be further processed, so that the extraction of the frequency
generates meaningful values of the frequency.
[0018] In an embodiment of the method according to the invention,
the physiological signal represents a respiration and/or pulse of a
patient. The respiration and the pulse represent the most important
physiological signals. The frequency of the signal is then
represented as the respiration rate and/or the pulse rate.
[0019] In an embodiment of the method according to the invention,
the predefined parameters characterize the physiological signal as
a function of time, frequency and/or position in space. Parameters
as a function of time can be, for example, signal variance,
peak-to-peak value and temporal correlation. Parameters as a
function of frequency can be, for example, dominant frequency and
spectral entropy. Parameters as a function of position in space can
be, for example, correlation between axes representing the position
in the Cartesian coordinate system in case a multi-axis
accelerometer is used as a physiological signal sensing device,
where a respiration movement usually takes place in one plane of
the sensor and thus signals from the two axes in this plane have a
strong correlation whereas the axis perpendicular to this plane
mainly measures noise, which will help distinguishing a good
physiological measurement from one contaminated by motion.
[0020] In an embodiment of the method according to the invention,
the step of classifying is performed only for the part of the next
time period that does not overlap with the previous time period in
the case of an overlap between the next and previous time period.
By applying an overlap of two consecutive time periods, an improved
resolution in time is achieved and the reliability and robustness
of the classification is improved. For example, in case of an
overlap of 50% of two consecutive time periods, half of the data in
the current time period is from the previous time period that is
already analyzed and classified in a previous step, and half of the
data in the current time period is new and not yet analyzed. The
classifying step uses the parameter values derived for the entire
current time period but only classifies or labels the new part of
the time period, for example as good or bad. The amount of overlap
between consecutive time periods can be optimized for a good
trade-off of the classification resolution in time and motion
artifacts propagation over consecutive time periods.
[0021] In an embodiment of the method according to the invention,
the step of classifying is also based upon a statistical database
of the set of predefined parameters. The increased set of input
data used for the classification step provides for an improved
reliability of the classification of the physiological signal.
[0022] In an embodiment of the method according to the invention,
the method further includes a step of conditioning the
physiological signal before the step of repeatedly collecting the
physiological signal, wherein the step of repeatedly collecting the
physiological signal comprises repeatedly collecting the
conditioned signal. For example, the signal conditioning comprises
a filtering of the physiological signal such that frequencies
corresponding to frequencies that are representative for the cyclic
physiological signal pass the filtering step. This reduces noise
and possible further unwanted environmental influences on the
physiological signal. For example, if the physiological signal
should be indicative of respiration, the conditioning step
preferentially filters the physiological signal such that
frequencies corresponding to possible frequencies of the
respiration motion pass the filtering step. In this case,
frequencies within a frequency range between 0 Hz and 2 Hz
preferentially pass the filtering step. As another example, if the
physiological signal is indicative of the heart activity of a
person, the filtering step preferentially filters the accelerometer
signals such that frequencies corresponding to possible frequencies
of heart activity motion pass the filtering unit, for example in
case an acceleration sensor is used, the conditioning step can be
adapted to filter the physiological signals such that frequencies
within the frequency range of between 5 Hz and 20 Hz pass the
filtering step, since in this frequency range the mechanical
vibrations caused by the beating heart, which corresponds to a
heart rate occurring in a range between approximately 0.5 Hz to 4
Hz or approximately 30 to 240 beats per minute, are captured by the
accelerometer. After taking the envelope of the filtered signal, a
bandpass filter between 0.5 Hz and 4 Hz is applied for generating a
signal for the heart rate calculation. A combination of respiration
and heart activity filtering could also be implemented. By
conditioning the signal before it is analyzed, for example by noise
filtering and/or signal normalization, gross contaminations are
filtered which results in an improved and more reliable and robust
signal analysis.
[0023] In an embodiment of the method according to the invention,
multiple physiological signals are collected simultaneously and the
steps of extracting and classifying are performed for each of the
multiple physiological signals separately. For example,
physiological signals measured at different locations of a body of
a patient may react to motion artifacts differently, thereby
creating complementary classified physiological signals within
corresponding time periods, which leads to an increased
availability of useful physiological signals in time as well as a
more robust and reliable classification.
[0024] In a second aspect of the present invention an apparatus for
monitoring a cyclic physiological signal is provided, the apparatus
comprising: [0025] a sensor adapted for measuring the cyclic
physiological signal; [0026] a time period definition unit adapted
for repeatedly defining a time period covering two or more cycles
of the cyclic physiological signal, in which time period the
physiological signal is analyzed, wherein a next time period is
adjacent to or overlaps with a previous time period; [0027] an
extraction unit adapted for extracting values for a set of
predefined parameters from the physiological signal within the time
period which parameters characterize the physiological signal
within the time period; and [0028] a classifying unit adapted for
classifying the physiological signal within the time period based
upon the extracted set of predefined parameter values.
[0029] In an embodiment of the apparatus according to the
invention, the sensor comprises a multi-axial accelerometer and the
measured physiological signal comprises multiple sub-signals
corresponding to the multiple axes and which sub-signals are
analyzed synchronously in time. A multi-axial accelerometer is a
device that measures the acceleration in multiple sensing axes, and
may for example be used as an inclinometer to reflect the abdomen
or chest movement caused by respiration or to measure the
mechanical vibration of the body surface reflecting the heart
activity. The multi-axial accelerometer is, for example, a
tri-axial accelerometer being adapted to generate three
accelerometer signals indicative of the acceleration along three
orthogonal spatial axes, wherein the time period definition unit is
adapted to combine these three accelerometer signals for analyzing
the combined signal. It is preferred that the multi-axial
accelerometer is adapted to be positioned at a body part of a
person, wherein the measured signal is a motion signal indicative
of at least one of respiration and heart activity of a person. For
generating a motion signal indicative of respiration the
multi-axial accelerometer is preferentially positioned at the
costal arch, roughly half way between the central and lateral
position. However, the multi-axial accelerometer can also be
located at other positions, for example, on the abdomen, in
particular, if limitations due to body physique like post-surgery
wounds apply. For generating a motion signal indicative of heart
rate the multi-axial accelerometer is preferentially positioned on
the left side of the abdomen/thorax. It is further preferred that
the accelerometer is positioned at the costal arch, in particular,
at the cartilage of the left lower ribs. A further preferred
position of the multi-axial accelerometer for generating a motion
signal indicative of heart rate is a higher position on the thorax
or a lower position on the abdomen. In particular, the preferred
positions for determining a motion signal indicative of respiration
are also preferred for measuring a motion signal indicative of
heart rate. Especially, for generating a motion signal indicative
of respiration and heart rate the multi-axial accelerometer is
preferentially positioned at the costal arch, half way
central-lateral on the left side.
[0030] In an embodiment of the apparatus according to the
invention, the apparatus further comprises a frequency
determination unit for determining the value of the frequency of
the classified physiological signal. In a preferred embodiment the
apparatus comprises multiple sensors and is suitable for measuring
multiple physiological signals each of which is analyzed either
separately or in combination.
[0031] It shall be understood that a preferred embodiment of the
invention can also be any combination of the dependent claims with
the respective independent claim.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] These and other aspects of the invention will be apparent
from and elucidated with reference to the embodiment(s) described
hereinafter. In the following drawings:
[0033] FIG. 1 shows a flowchart exemplarily illustrating an
embodiment of a method of processing a cyclic physiological
signal,
[0034] FIG. 2 shows schematically and exemplarily an example of
defining the time periods for segmented signal analysis,
[0035] FIG. 3 shows a flowchart exemplarily illustrating another
embodiment of a method of processing a cyclic physiological
signal,
[0036] FIG. 4 shows a flowchart exemplarily illustrating another
embodiment of a method of processing a cyclic physiological
signal,
[0037] FIG. 5 shows a flowchart exemplarily illustrating another
embodiment of a method of processing a cyclic physiological
signal,
[0038] FIG. 6 shows schematically and exemplarily an example of a
classification of a cyclic physiological signal,
[0039] FIG. 7 shows schematically and exemplarily another example
of a classification of a cyclic physiological signal measured by a
tri-axial accelerometer, and
[0040] FIG. 8 shows schematically and exemplarily an embodiment of
an apparatus adapted for processing a cyclic physiological
signal.
DETAILED DESCRIPTION OF EMBODIMENTS
[0041] FIG. 1 shows a flowchart exemplarily illustrating an
embodiment of a method of processing a cyclic physiological signal.
In step 1 a cyclic physiological signal is captured, in this
embodiment with a sensor located on a suitable location of an
object, in this example a person. The person may be a patient in an
intensive care department of a hospital, but also a patient in a
general ward department of a hospital, in which the patient is more
mobile and is monitored less severe than in the intensive care
environment. Furthermore, the person could be located in his own
home environment. The sensor may, for example, comprise a
multi-axial accelerometer which is adapted to generate
accelerometer signals indicative of the acceleration along
different spatial axes. In this embodiment, the multi-axial
accelerometer is a tri-axial accelerometer being adapted to
generate three accelerometer signals indicative of the acceleration
along three orthogonal spatial axes. For example, tri-axial
accelerometers named ST Microelectronics LIS344ALH or Kionix KXM52
can be used. However, also other kinds of multi-axial
accelerometers can be used for generating accelerometer signals
indicative of the acceleration along different spatial axes. The
cyclic physiological signal may be the respiration or the heart
beat of the person. Respiration rate is one of the most important
vital body signs in patient monitoring and it has proven to be a
good indicator of the deterioration of patient conditions, and it
plays a crucial role in early warning hospital systems in
combination with other vital body signs, such as the heart
rate.
[0042] In step 2 of FIG. 1 a time period is defined and the
physiological signal is collected over that time period. The time
period covers two or more cycles of the physiological signal,
preferably at least five cycles. The time period is typically a few
to a few tens of seconds depending on the type of physiological
signal that is monitored. For example, the time period for
respiration signal classification may be chosen from 30 seconds up
to one minute, in which about 5 to 30 breaths may be covered, which
is a typical frequency range of respiration.
[0043] In step 3 of FIG. 1 values are extracted for a set of
predefined parameters from the signal segment within the time
period that was defined in step 2. The parameters and their values
characterize the signal segment within the current time period in
various aspects such as in time, in frequency and in spatial
coordinates, i.e. the position in space. To grasp distinct features
of, for example, a respiration or heart beat signal, specific
characteristics of the signal can be defined. Characteristic
parameters as a function of time are, for example, signal variance,
peak-to-peak value and temporal correlation. Characteristic
parameters as a function of frequency are, for example, dominant
frequency and spectral entropy. A characteristic parameter as a
function of spatial coordinates is, for example, the correlation
between the three orthogonal spatial axes as measured with a
three-axial accelerometer. A typical respiration signal measured by
an accelerometer is of low signal variance, periodic with a
frequency ranging from 0.05 Hz to 2 Hz and has strong inter-axis
correlation. The extracted parameter values map each signal segment
to a point in the parameters space.
[0044] In step 4 of FIG. 1 the signal segment within time period
defined in step 2 is classified based on the set of predefined
parameter values that were extracted in step 3. The classification
may be a straightforward good-bad classification of the signal
segment within the current time period, indicating whether the
signal in the current time period resembles, for example, a
respiration signal or not. The classification may also result in an
indication as to what extent the physiological signal is
contaminated by, for example, motion artifacts.
[0045] In step 10 of FIG. 1 it is checked if a next time period
should be defined. If no next time period has to be defined, for
example because the end of the signal is reached, then the method
stops in step 11. If a next time period can be and/or should be
defined, then the method returns to step 2 and a next time period
is defined. The next time period may be adjacent to the previous
time period. Alternatively, the next time period overlaps the
previous time period. By applying an overlap of two consecutive
time periods, an improved resolution in time is achieved. After the
definition of the next time period, the method continues with step
3 in which a set of predefined parameter values is extracted for
the signal segment that is within this next time period, followed
by step 4 in which the signal segment located within this next time
period is classified based on the set of predefined parameter
values that were extracted in the previous step. In case this next
time period overlaps the previous time period, for example, in case
of an overlap of 50% of two consecutive time periods, half of the
data in the current time period is from the previous time period
that is already analyzed and classified in the previous step, and
only half of the data in the current time period is new and not
analyzed. In this case the classifying step uses the parameter
values derived for the entire current time period but only
classifies or labels the new part of the time period, for example
as good or bad.
[0046] FIG. 2 shows schematically and exemplarily an example of a
respiration signal 30 as a function of time in which time periods
31, 32, 33 are defined for a segmented signal analysis. The
horizontal axis of the graph in FIG. 2 represents time in arbitrary
units and the vertical axis represents the amplitude of the
respiration signal in arbitrary units. FIG. 2 illustrates that this
embodiment applies time periods that overlap consecutively. The
first time period 31 overlaps with the second time period 32 and
the second time period 32 overlaps with the third time period 33.
The other time periods, following after time period 33, are not
shown in the graph. According to the method illustrated by the
flowchart of FIG. 1, in step 2 the first time period 31 is defined
first. The part of the respiration signal 30 that is within the
first time period 31 is analyzed in steps 3 and 4 of FIG. 1
comprising first an extraction of the values for a first set of
predefined parameters and then a classification of the part of the
signal 30 that is within the first time period 31. In the next loop
the second time period 32 is defined and the data of the part of
the signal 30 that are within the second time period 32 are
collected and used for extracting the values of a second set of
predefined parameters for the signal segment within this second
time period 32. Because the part of the first time period 31 that
overlaps with the second time period 32 is already classified in
the previous classification step, in this embodiment, only the part
of the signal 30 is classified that is within the part of the
second time period 32 that does not overlap with the first time
period 31. Then the third time period 33 is defined and the data of
the part of the signal 30 that is within the third time period 33
are collected and used for extracting the values of a third set of
predefined parameters for the signal segment within this third time
period 33. Because the part of the second time period 32 that
overlaps with the third time period 33 is already classified in the
previous classification step, in this embodiment, only the part of
the signal 30 is classified that is within the part of the third
time period 33 that does not overlap with the second time period
32. These steps are repeated until the whole signal 30 is covered
with consecutive time periods (not shown). The amount of overlap
between consecutive time periods can be optimized for a good
trade-off of the classification resolution in time and motion
artifacts propagation over consecutive time periods. The time
periods, each classifying a time segment of the signal 30, slide
over the signal 30 thereby providing a segmented signal analysis
and classification.
[0047] FIG. 3 shows a flowchart exemplarily illustrating another
embodiment of a method of processing a cyclic physiological signal.
The method illustrated in FIG. 3 is an extension of the method that
is illustrated in FIG. 1. Steps 1, 2, 3, 4, 10 and 11 of FIG. 1 are
the same steps in FIG. 3. In this embodiment, after step 1 a step
12 provides a conditioning of the signal captured in step 1. The
conditioning of the signal may comprise, for example, a filtering
of the signal to improve the signal quality before it is analyzed
in the next steps of the method. For example, step 12 comprises
filtering of the signal such that frequencies corresponding to
possible frequencies of respiration or of heart activity are
passed. In particular, the filtering step can be adapted to filter
frequencies such that a typical heart rate or frequency range
between 0.5 Hz and 4 Hz is passed. It should be noted that in a
frequency range between approximately 5 Hz and 20 Hz the mechanical
vibrations caused by the beating heart, which corresponds to a
heart rate occurring in a range between approximately 0.5 Hz to 4
Hz or approximately 30 to 240 beats per minute, are captured by the
accelerometer. After taking the envelope of the filtered signal, a
bandpass filter, filtering the frequency range of the heart rate,
is applied for generating a signal for the heart rate calculation.
It is also possible that for determining respiration frequencies
the signal is filtered in a frequency range between 0 Hz and 2 Hz
and that for determining heart rate frequencies the signal is
filtered such that only a frequency range between approximately 0.5
Hz and 4 Hz passes. A combination of these two frequency ranges or
another frequency range may also be an option. In this embodiment
in step 2 the conditioned physiological signal is collected over
the selected time period.
[0048] In step 5 of FIG. 3 it is decided if the signal segment
within the current time period is good or bad, i.e. to be accepted
or to be rejected, based on the results of the classification of
the signal segment in previous step 4. For example in case of a
respiration signal, the signal segment within the current time
period is classified as good or acceptable if it resembles a
breathing signal, and it is classified as bad or is rejected if it
does not resemble a breathing signal because of, for example,
contamination by motion artifacts as a result of physical movement
of the person. If the signal segment within the current time period
is accepted in step 5, then in step 6 the frequency or rate of the
cyclic signal is calculated for the part of the signal that is
within the current time period. Because, the signal segment is
classified as good and thus resembles the required physiological
signal, the value of the frequency or rate calculated for this
signal segment will be a reliable value. For example, in case of a
respiration signal in step 6 the respiration rate is calculated and
in case of a heart beat signal the pulse rate is calculated. The
calculated respiration rate and/or pulse rate can be shown on a
display (not shown). On the other hand, if the signal segment
within the current time period is rejected in step 5, then no
frequency or rate will be calculated for the signal segment within
this time period. In this case the signal within the current time
period does not resemble a required physiological signal, for
example a breathing or heart beat signal, and a calculation of a
respiration rate or a pulse rate for this signal segment will
result in an inaccurate value. To enhance the visibility of
rejected signal segments, the signal segment in this time period
can be labeled as bad or rejected by overlaying a colored bar over
the signal for this time period on a display showing the signal as
a function of time.
[0049] Various classification algorithms may be used for the
good/bad signal classification. Commonly used classifiers include
rule-based, Bayesian, artificial neural network, decision tree,
linear discriminant function and k nearest neighbor classifiers.
The selection and design of such a respiration-specific classifier
may include the use of a statistically complete respiration
database of mobile subjects, with which a chosen classifier is
trained and its classification performance is evaluated. Criteria,
such as computational complexity and interpretability of the
algorithm, can also be important in the selection of the
classifier. The resultant classifier is usually a good trade-off of
multiple criteria. Note that the generation of the good/bad
classifier is completed offline, and the execution of
classification is instant and computationally light, enabling a
real-time physiological signal analysis. FIG. 4 shows a flowchart
exemplarily illustrating another embodiment of a method of
processing a cyclic physiological signal. The method illustrated in
FIG. 4 is an extension of the method that was illustrated in FIG.
3. Similarly numbered steps of FIG. 3 are the same steps in FIG. 4.
In this embodiment, after step 6 in a step 7 a confidence index of
the calculated frequency is calculated for the part of the signal
in the present time period if this signal segment is classified as
acceptable. The confidence index indicates the confidence or the
accuracy of the calculated frequency in step 6. The confidence
index may be defined using the values of the parameters extracted
in step 4 for the corresponding signal segment in combination with
the calculated value of the frequency, for example the value of the
respiration rate or heart rate. The confidence index provides
useful input in further processing of the respiration rate or heart
rate data, for example in proper reporting of the respiration rate
and appropriate respiration rate trend analysis.
[0050] FIG. 5 shows a flowchart exemplarily illustrating another
embodiment of a method of processing a cyclic physiological signal.
The method illustrated in FIG. 5 is an extension of the method that
was illustrated in FIG. 4. Similarly numbered steps of FIG. 4 are
the same steps in FIG. 5. In FIG. 5 the signal is captured in step
1 with an accelerometer. When an accelerometer is used as a sensing
device, not only respiration but also heart beat or pulse
information can be measured. This is depicted in FIG. 5, where a
branch dedicated to heart signal analysis is incorporated next to
the already existing branch in which, in this embodiment, the
respiration signal is analyzed, comprising steps 3, 4, 5, 6 and 7.
Step 12 in this embodiment is adapted to filter the accelerometer
signals with a filter for filtering both the accelerometer signals
for determining respiration and the accelerometer signals for
determining heart rate. Alternatively, two separate and dedicated
filters may be applied, a first filter for filtering the
accelerometer signals for determining respiration and a second
filter for filtering the accelerometer signals for determining
heart rate. Because heart beating causes mechanical vibration of
the body surface that is measured as inertial acceleration by the
sensor (and small inclination changes), the pulse signal is treated
in a higher frequency band than for the respiration signal.
[0051] In step 13 of FIG. 5 values are extracted for a set of
predefined parameters from the pulse signal segment within the time
period that was defined in step 2. The parameters and their values
characterize the pulse signal segment within the current time
period in various aspects such as in time, in frequency and in
spatial coordinates, i.e. the position in space. To grasp distinct
features of the heart beat signal, specific characteristics of the
signal can be defined. Characteristic parameters of the pulse
signal as a function of time are, for example, signal variance,
signal mean and correlation in time. Characteristic parameters as a
function of frequency are, for example, dominant frequency and
spectral entropy. A characteristic parameter as a function of
spatial coordinates is, for example, the correlation between the
three orthogonal spatial axes as measured with a three-axial
accelerometer. A typical pulse signal measured by an accelerometer
is periodic with a frequency ranging between 0.5 Hz and 4 Hz and
has strong inter-axis correlation. It should be noted that in a
frequency range between approximately 5 Hz and 20 Hz the mechanical
vibrations caused by the beating heart, which corresponds to a
heart rate occurring in a range between approximately 0.5 Hz to 4
Hz or approximately 30 to 240 beats per minute, are captured by the
accelerometer. After taking the envelope of the filtered signal, a
bandpass filter, filtering the frequency range of the heart rate,
is applied for generating a signal for the heart rate calculation.
It should be noted that the definition of the time period in step 2
can also be done for the pulse signal and the respiration signal
separately.
[0052] In step 14 of FIG. 5 the pulse signal segment within time
period defined in step 2 is classified based on the set of
predefined parameter values that were extracted in step 13 in a
similar way as is done for the respiration signal in step 3 but
optimized and adapted for the heart beat, or pulse, signal. In step
15 of FIG. 5 it is decided if the pulse signal segment within the
current time period is good or bad, i.e. to be accepted or to be
rejected, based on the results of the classification in previous
step 14 in a similar way as is done for the respiration signal in
step 5, but now adapted for the characteristics of the pulse
signal. If the pulse signal segment within the current time period
is accepted in step 15, then in step 16 the pulse rate is
calculated for the part of the signal that is within the current
time period, and, in a similar way the respiration rate is
calculated in step 6. Because, the pulse signal segment is
classified as good and thus resembles a typical heart beat signal,
the value of the pulse rate calculated for this signal segment will
be a reliable value. The calculated respiration rate and pulse rate
can both be shown on a display (not shown). On the other hand, if
the pulse signal segment within the current time period is rejected
in step 15, then no pulse rate will be calculated for the pulse
signal segment within this time period. In this case the pulse
signal within the current time period does not resemble a heart
beat signal, and a calculation of the pulse rate for this pulse
signal segment will result in an inaccurate value of the pulse
rate. Similarly, in step 5 the respiration rate will not be
calculated in case the signal segment within the current time
period does not resemble a respiration signal. It is also possible
that within the same time period the heart beat signal is accepted
and the respiration signal is rejected, or vice versa. After step
16 in a step 17 a pulse rate confidence index of the calculated
pulse rate is calculated for the part of the pulse signal in the
present time period if this pulse signal segment is classified as
acceptable, in a similar way a respiration rate confidence index is
calculated in step 6 for the respiration signal segment. The pulse
rate confidence index indicates the confidence or the accuracy of
the pulse rate calculated in step 16. The pulse rate confidence
index may be defined using the values of the parameters extracted
in step 14 for the corresponding pulse signal segment in
combination with the calculated value of the pulse rate. The pulse
rate and respiration rate confidence indices provide useful input
in further processing of the respiration rate or pulse rate data,
for example in proper report of the respiration and/or pulse rate
and appropriate respiration and/or pulse rate trend analysis.
[0053] FIG. 6 shows schematically and exemplarily an example of a
classification of a respiration signal 40 applying a method
according to the invention. The horizontal axis of the graph in
FIG. 6 represents time in arbitrary units and the vertical axis
represents the amplitude of the respiration signal 40 in arbitrary
units. Shaded or colored bars indicate signal segments 41 that are
classified and/or labeled as bad or reject. The respiration signal
40 that is not within the signal segments 41 are classified as good
or acceptable signal segments. From FIG. 6 it is clear that the
rejected signal segments 41 are contaminated by, for example,
motion artifacts and that the signal segments that are not rejected
better reflect a breathing signal than the rejected signal segments
41.
[0054] FIG. 7 shows schematically and exemplarily another example
of a classification of respiration signals 52, 53, 54 measured by a
tri-axial accelerometer. FIG. 7 shows three graphs, each reflecting
the respiration signal captured for the corresponding spatial axis
of the tri-axial accelerometer. The horizontal axis of each graph
in FIG. 7 represents time in arbitrary units and the vertical axis
represents the amplitude of the x-axis accelerometer respiration
signal 52 captured for the spatial x-axis in FIG. 7a, of the y-axis
accelerometer respiration signal 53 captured for the spatial y-axis
in FIG. 7b and of the z-axis accelerometer respiration signal 54
captured for the spatial z-axis in FIG. 7c. The signal analysis can
be performed in parallel for each of the signals 52, 53, 54,
wherein in the classification step a comparison between the three
different classification results can be done to provide a combined
classification of the current time period. Alternatively, the
classification is performed on one signal which is a combination of
the three signals 52, 53, 54. Shaded or colored bars indicate
signal segments 51 that are classified and/or labeled as rejected
and from which it is clear that the rejected signal segments 51 are
contaminated by, for example, motion artifacts and that the signal
segments that are not rejected better reflect a breathing signal
than the rejected signal segments 51.
[0055] The method according to the invention may be executed by a
computer program adapted to carry out the steps as defined in the
method according to the invention.
[0056] FIG. 8 shows schematically and exemplarily an embodiment of
an apparatus adapted for processing a cyclic physiological signal.
A sensor 101 captures a cyclic physiological signal from an object,
for example a patient. The captured physiological signal is input
for a time period definition unit 102 in which a time period is
defined for which time period the signal data are collected. The
time period definition unit 102 is adapted to repeatedly define a
consecutive time period in which a next time period is adjacent to
or overlaps with a previous time period. The part of the signal
contained in the time period is input for an extraction unit 103,
which extracts a set of predefined parameter values from the signal
segment in the current time period which has been collected and
defined in the time period definition unit 102. The parameters and
their values define characteristics that are specific for the
physiological signal and the values of the parameters that are
extracted for the current signal segment thus characterize the
physiological signal segment within the current time period. The
extracted parameter values are input for a classification unit 104,
which classifies the signal within the current time period based
upon the extracted parameter values. In this embodiment, the
classified signal is input for a frequency calculation unit 105,
which calculates the frequency of the classified cyclic
physiological signal based upon the classification results and,
optionally, also based on the extracted parameter values.
[0057] The monitoring apparatus preferably comprises one or more
multi-axial accelerometers, in particular, two tri-axial
accelerometers, for being positioned at the person at complementary
positions, preferentially at the chest and/or abdomen of a person,
in order to monitor respiration and/or heart rate, in particular,
under ambulatory conditions. The multi-axial accelerometer is used
as an inclinometer to reflect the movement of the object, in
particular, to reflect the movement of the abdomen or the chest
caused by respiration and/or heart activity. The movement is
reflected by an inclination change of a surface of the object, on
which the multi-axial accelerometer is positioned. The several
spatial axes of the multi-axial accelerometer, which are
preferentially three orthogonal axes, record the accelerometer
signals equal to the projection of the gravity vector on each of
these axes. Preferably, the extraction unit and the classification
unit are adapted to analyze the signals of the one or more
multi-axial accelerometers in parallel.
[0058] The monitoring apparatus and the signal analysis method
according to the invention can be used for patient monitoring, in
particular, to aid in detecting the acutely ill patients outside
the intensive care areas.
[0059] Although in the above described embodiments the multi-axial
accelerometer has preferentially three orthogonal axes, the
multi-axial accelerometer can also have two orthogonal axes or more
than three axes. Furthermore, the spatial axis can also include
another angle, i.e. in another embodiment the axes can be
non-orthogonal.
[0060] Although in the above described embodiments, one or two
multi-axial accelerometers are used, also more than two
accelerometers can be used for determining a breathing rate and/or
a heart rate.
[0061] Other variations to the disclosed embodiments can be
understood and effected by those skilled in the art in practicing
the claimed invention, from a study of the drawings, the
disclosure, and the appended claims.
[0062] In the claims, the word "comprising" does not exclude other
elements or steps, and the indefinite article "a" or "an" does not
exclude a plurality.
[0063] A single unit or device may fulfill the functions of several
items recited in the claims. The mere fact that certain measures
are recited in mutually different dependent claims does not
indicate that a combination of these measures cannot be used to
advantage.
[0064] A computer program may be stored/distributed on a suitable
medium, such as an optical storage medium or a solid-state medium,
supplied together with or as part of other hardware, but may also
be distributed in other forms, such as via the Internet or other
wired or wireless telecommunication systems.
[0065] Any reference signs in the claims should not be construed as
limiting the scope.
* * * * *