U.S. patent application number 15/064107 was filed with the patent office on 2016-06-30 for system and method of detecting abnormal movement of a physical object.
The applicant listed for this patent is INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE. Invention is credited to Chuan-Wei TING, Ching-Yao WANG.
Application Number | 20160183811 15/064107 |
Document ID | / |
Family ID | 46317959 |
Filed Date | 2016-06-30 |
United States Patent
Application |
20160183811 |
Kind Code |
A1 |
TING; Chuan-Wei ; et
al. |
June 30, 2016 |
SYSTEM AND METHOD OF DETECTING ABNORMAL MOVEMENT OF A PHYSICAL
OBJECT
Abstract
A method of identifying a predetermined type of abnormal
movement of a physical object includes generating a raw matrix
comprising a first array and a second array, the generation of the
raw matrix. The method also includes generating an integrated
matrix by performing a dimension reduction on the raw matrix. The
method a further includes identifying the predetermined type of
abnormal movement of the physical object by comparing the
integrated matrix or a set of indexes derived from the integrated
matrix with a predetermined benchmark pattern corresponding to the
predetermined type of abnormal movement. Generating the raw matrix
includes a first analysis on a predetermined portion of a periodic
signal representative of movement of the physical object to
generate the first array. Generating the raw matrix also includes
performing a second analysis different from the first analysis on
the predetermined portion of the periodic signal to generate the
second array.
Inventors: |
TING; Chuan-Wei; (Kaohsiung,
TW) ; WANG; Ching-Yao; (Guiren Township, TW) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE |
Hsinchu |
|
TW |
|
|
Family ID: |
46317959 |
Appl. No.: |
15/064107 |
Filed: |
March 8, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14012873 |
Aug 28, 2013 |
9314177 |
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15064107 |
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12979570 |
Dec 28, 2010 |
8543194 |
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14012873 |
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Current U.S.
Class: |
600/509 ;
702/150 |
Current CPC
Class: |
A61B 5/0245 20130101;
A61B 5/0452 20130101; A61B 5/4803 20130101; A61B 5/04012 20130101;
A61B 5/0816 20130101 |
International
Class: |
A61B 5/0245 20060101
A61B005/0245; A61B 5/00 20060101 A61B005/00; A61B 5/04 20060101
A61B005/04; A61B 5/08 20060101 A61B005/08 |
Claims
1. A method of identifying a predetermined type of abnormal
movement of a physical object, a periodic signal being
representative of movement of the physical object, the method
comprising: generating a raw matrix comprising a first array and a
second array, the generation of the raw matrix comprising:
performing a first analysis on a predetermined portion of the
periodic signal to generate the first array; and performing a
second analysis different from the first analysis on the
predetermined portion of the periodic signal to generate the second
array; generating an integrated matrix by performing a dimension
reduction on the raw matrix; and identifying the predetermined type
of abnormal movement of the physical object by comparing the
integrated matrix or a set of indexes derived from the integrated
matrix with a predetermined benchmark pattern corresponding to the
predetermined type of abnormal movement.
2. The method of claim 1, wherein the identification of the
predetermined type of abnormal movement comprises determining a
likelihood of the predetermined type of abnormal movement.
3. The method of claim 2, further comprising: outputting the
likelihood of the predetermined type of abnormal movement of the
physical object to a display.
4. The method of claim 1, wherein the first analysis or the second
analysis is a time-domain analysis, a pattern analysis, or deriving
a feature from the predetermined duration of the periodic signal
obtained according to a first spatial measurement configuration and
at least a portion of another periodic signal being representative
of the movement obtained according to a second spatial measurement
configuration.
5. The method of claim 1, wherein the dimension reduction is
performed by applying principal component analysis, factor
analysis, or independent component analysis on the raw matrix.
6. The method of claim 1, wherein the periodic signal is obtained
by detecting one or more of the following activities: heartbeat,
breathing, speech, earthquake or seismic activity, orbital movement
of an astronomical object, periodic variances of an astronomical
object, movement of a piston, or rotation of a motor.
7. The method of claim 1, wherein the first analysis is a mutual
information analysis, comprising: segmenting the predetermined
portion of the periodic signal into a plurality of signal segments,
each signal segment including a nominal waveform, and the plurality
of signal segments including one or more pairs of adjacent signal
segments; for each pair of the one or more pairs of adjacent signal
segments: obtaining two signal arrays having the same size by
re-sampling one or both of the adjacent signal segments; and
calculating joint probability and marginal probability of the two
signal arrays of the re-sampled adjacent signal segments; and
calculating a mutual information index based on the calculated
joint probability and the calculated marginal probability of the
one or more pairs of re-sampled adjacent signal segments.
8. The method of claim 7, wherein the calculation of the mutual
information index is performed based on an equation of: MI ( X ; Y
) = x y p ( x , y ) log p ( x , y ) p ( x ) p ( y ) ##EQU00002##
where x and y each represents components of one of the two signal
arrays of the re-sampled adjacent signal segments, p(x,y)
represents the joint probability of the two signal arrays, and p(x)
and p(y) each represents the marginal probability of one of the two
signal arrays of the re-sampled adjacent signal segments.
9. A system comprising: a computer readable storage medium encoded
with a computer program code; a processor coupled to the computer
readable storage medium, the processor being configured to execute
the computer program code; wherein the computer program code is
configured to cause the processor to: perform a first analysis on a
predetermined portion of a periodic signal to generate a first
array, the periodic signal being representative of movement of a
physical object, and the predetermined portion corresponding to a
predetermined time period of the periodic signal; perform a second
analysis different from the first analysis on the predetermined
portion of the periodic signal to generate the second array;
generate an integrated matrix by performing a dimension reduction
on the raw matrix; and determine a likelihood of a predetermined
type of abnormal movement of the physical object by comparing the
integrated matrix or a set of indexes derived from the integrated
matrix with a predetermined benchmark pattern corresponding to the
predetermined type of abnormal movement.
10. The abnormality analysis system of claim 9, wherein the first
analysis is a mutual information analysis, and the computer program
code is further configured to cause the processor to: segment the
predetermined duration of the periodic signal into a plurality of
signal segments, each signal segment including a nominal waveform,
and the plurality of signal segments including one or more pairs of
adjacent signal segments; for each pair of the one or more pairs of
adjacent signal segments: obtain two signal arrays having the same
size by re-sampling one or both of the re-sampled adjacent signal
segments; and calculate joint probability and marginal probability
of the two signal arrays of the adjacent signal segments; and
calculate a mutual information index based on the calculated joint
probability and the calculated marginal probability of the one or
more pairs of re-sampled adjacent signal segments.
11. The abnormality analysis system of claim 10, wherein the
computer program code is further configured to cause the processor
to calculate the mutual information index based on an equation of:
MI ( X ; Y ) = x y p ( x , y ) log p ( x , y ) p ( x ) p ( y )
##EQU00003## where x and y each represents components of one of the
two signal arrays of the re-sampled adjacent signal segments,
p(x,y) represents the joint probability of the two signal arrays,
and p(x) and p(y) each represents the marginal probability of one
of the two signal arrays of the re-sampled adjacent signal
segments.
12. The abnormality analysis system of claim 9, wherein the
identification of the predetermined type of abnormal movement
comprises determining a likelihood of the predetermined type of
abnormal movement.
13. The abnormality analysis system of claim 12, wherein the
computer program code is further configured to cause the processor
to: output the likelihood of the predetermined type of abnormal
movement of the physical object to a display.
14. The abnormality analysis system of claim 9, wherein the first
analysis or the second analysis is a time-domain analysis, a
pattern analysis, or deriving a feature from the predetermined
duration of the periodic signal obtained according to a first
spatial measurement configuration and at least a portion of another
periodic signal being representative of the movement obtained
according to a second spatial measurement configuration.
15. The abnormality analysis system of claim 9, wherein the
computer program code is further configured to cause the processor
to: perform the dimension reduction by applying principal component
analysis, factor analysis, or independent component analysis on the
raw matrix.
16. The abnormality analysis system of claim 9, wherein the
computer program code is further configured to cause the processor
to: obtain the periodic signal by detecting one or more of the
following activities: heartbeat, breathing, speech, earthquake or
seismic activity, orbital movement of an astronomical object,
periodic variances of an astronomical object, movement of a piston,
or rotation of a motor.
17. A method, comprising: generating a first array of a raw matrix
based on a predetermined portion of a periodic signal
representative of a movement of a physical object, wherein
generating the first array comprises: segmenting the predetermined
portion of the periodic signal into a plurality of signal segments,
each signal segment including a nominal waveform, and the plurality
of signal segments including one or more pairs of adjacent signal
segments; obtaining two signal arrays having a same size by
re-sampling one or both of the adjacent signal segments for each
pair of the one or more pairs of adjacent signal segments;
calculating joint probability and marginal probability of the two
signal arrays of the re-sampled adjacent signal segments; and
calculating a mutual information index based on the calculated
joint probability and the calculated marginal probability of the
one or more pairs of re-sampled adjacent signal segments;
generating a second array of the raw matrix based on the
predetermined portion of the periodic signal; and recognizing the
movement of the physical object corresponds to one or more
predefined types of abnormal movement based on a comparison of data
derived from the raw matrix with a benchmark pattern corresponding
to the one or more predefined types of abnormal movement.
18. The method of claim 17, further comprising: calculating a
likelihood of the one or more predefined types of abnormal
movements to which the movement of the physical object
corresponds.
19. The method of claim 17, further comprising: outputting the one
or more predefined types of abnormal movements to which the
movement of the physical object corresponds to a display.
20. The method of claim 17, wherein the periodic signal is obtained
by detecting one or more of a heartbeat, breathing, speech, an
earthquake, a seismic activity, an orbital movement of an
astronomical object, periodic variances of an astronomical object,
a movement of a piston, or a rotation of a motor.
Description
CLAIM OF PRIORITY
[0001] The present application is a divisional of U.S. application
Ser. No. 14/012,873, filed Aug. 28, 2013, which is a divisional of
U.S. application Ser. No. 12/979,570, filed Dec. 28, 2010, now U.S.
Pat. No. 8,543,194, issued Sep. 24, 2013, which are incorporated by
reference herein in their entireties.
BACKGROUND
[0002] Some physical objects incorporate or encompass cyclic or
periodic motion, such as an electrical motor having a rotor that
spins at a rotational speed or a human heart beating (i.e.,
performing contraction and relaxation) at a heart rate. The cyclic
movement of the physical objects is observable or recordable by
detection systems in the form of periodic or substantially periodic
signals. The term "periodic or substantially periodic signals"
(hereinafter also referred to as "periodic signals") refers to the
nature of the detected signals that usually have repetitive nominal
waveform patterns although the exact waveforms and frequencies
vary. Abnormal movement of a given physical object is thus
detectable by analyzing the periodic signals.
[0003] For example, an Electrocardiograph (ECG) device is capable
of converting the movement of a heart into one or more ECG signals
from one or more combinations of leads attached to a person or
animal undergoing examination. A trained medical care provider may
identify certain abnormal movement(s) of the observed heart by
comparing the ECG signals with a benchmark ECG signal of normal
movement.
SUMMARY
[0004] In accordance with one embodiment, a method of detecting
abnormal movement of a physical object is disclosed. According to
some embodiments, a periodic signal is representative of movement
of the physical object, and the method includes: generating a raw
matrix comprising a first array and a second array; generating an
integrated matrix by performing a dimension reduction on the raw
matrix; and determining a likelihood of a predetermined type of
abnormal movement of the physical object by comparing the
integrated matrix or a set of indexes derived from the integrated
matrix with a predetermined benchmark pattern corresponding to the
predetermined type of abnormal movement.
[0005] In some embodiments, the generation of the raw matrix
includes: performing a first analysis on a predetermined portion of
the periodic signal to generate the first array, the predetermined
portion corresponding to a predetermined time period of the
periodic signal; and performing a second analysis different from
the first analysis on the predetermined portion of the periodic
signal to generate the second array.
[0006] In some embodiments, the first analysis or the second
analysis is a time-domain analysis, a pattern analysis, or deriving
a feature from the predetermined duration of the periodic signal
obtained according to a first spatial measurement configuration and
at least a portion of another periodic signal being representative
of the movement obtained according to a second spatial measurement
configuration.
[0007] In accordance with another embodiment, a method of
identifying a predetermined type of abnormal movement of a physical
object is disclosed. The method includes generating a raw matrix
including a first array and a second array. The generation of the
raw matrix includes performing a first analysis on a predetermined
portion of a periodic signal to generate the first array; and
performing a second analysis different from the first analysis on
the predetermined portion of the periodic signal to generate the
second array. The periodic signal is representative of movement of
the physical object. An integrated matrix is generated by
performing a dimension reduction on the raw matrix. The
predetermined type of abnormal movement of the physical object is
identified by comparing the integrated matrix or a set of indexes
derived from the integrated matrix with a predetermined benchmark
pattern corresponding to the predetermined type of abnormal
movement.
[0008] In accordance with another embodiment, a system for
performing the disclosed methods and computer readable storage
medium being encoded with a computer program code which when
executed by a processor causes the processor to perform the
disclosed methods are also disclosed.
[0009] In accordance with another embodiment, a method comprises
generating a first array of a raw matrix based on a predetermined
portion of a periodic signal representative of a movement of a
physical object. Generating the first array comprises segmenting
the predetermined portion of the periodic signal into a plurality
of signal segments, each signal segment including a nominal
waveform, and the plurality of signal segments including one or
more pairs of adjacent signal segments. Generating the first array
also comprises obtaining two signal arrays having a same size by
re-sampling one or both of the adjacent signal segments for each
pair of the one or more pairs of adjacent signal segments.
Generating the first array further comprises calculating joint
probability and marginal probability of the two signal arrays of
the re-sampled adjacent signal segments. Generating the first array
additionally comprises calculating a mutual information index based
on the calculated joint probability and the calculated marginal
probability of the one or more pairs of re-sampled adjacent signal
segments. The method also comprises generating a second array of
the raw matrix based on the predetermined portion of the periodic
signal. The method further comprises recognizing the movement of
the physical object corresponds to one or more predefined types of
abnormal movement based on a comparison of data derived from the
raw matrix with a benchmark pattern corresponding to the one or
more predefined types of abnormal movement.
[0010] As will be realized, one or more embodiments are capable of
other and different embodiments, and the several details are
capable of modification in various obvious respects, all without
departing from the described embodiments.
DESCRIPTION OF THE DRAWINGS
[0011] One or more embodiments are illustrated by way of example,
and not by limitation, in the figures of the accompanying drawings,
wherein elements having the same reference numeral designations
represent like elements throughout and wherein:
[0012] FIG. 1 is a plot of a waveform corresponding to an ECG
signal;
[0013] FIG. 2 is a flow chart of various methods of analyzing the
waveform of the ECG signal of FIG. 1 in conjunction with some
embodiments;
[0014] FIG. 3 is a flow chart of a method of analyzing periodic
signals representing movement of a physical object according to
some embodiments;
[0015] FIG. 4 is a flow chart of a method of detecting abnormal
movement of a physical object according to some embodiments;
[0016] FIG. 5 is a functional block diagram of an abnormality
analysis system usable for implementing the method disclosed in
FIGS. 2-4 according to some embodiments;
[0017] FIGS. 6A and 6B are charts of resulting data arrays of
various analyses performed on a benchmark ECG signal, integrated
matrices, and an annotated waveform corresponding to abnormal
movement according to some embodiments;
[0018] FIGS. 7A and 7B are charts of resulting data arrays of
performing asymmetric index (Al) of multi-scale analysis on results
of RR interval analysis and an integrated matrix derived from ECG
signals of end-stage renal disease (ESRD) patients according to
some embodiments.
DETAILED DESCRIPTION
[0019] FIG. 1 is a plot of a waveform 100 corresponding to an ECG
signal of a human heart where the signal has been segmented and
categorized into various segments (intervals) and feature points.
Some of the segments and feature points of the ECG signal include
P-wave 110, T-wave 120, QRS complex 130 that further includes a
Q-point 132, an R-point 134, and an S-point 136, a PR-segment 140,
and a ST-segment 150. A complete cardiac cycle C thus includes a
P-wave 110 section, a PR-segment 140, a Q-point 132, an R-point
134, an S-point 136, a ST-segment 150, and a T-wave 120 section. In
some embodiments, more segments and/or feature points are defined
for various information processing purposes.
[0020] ECG signal sections and feature points are defined in order
to facilitate the analysis of heart movement. In practice, illness
or disease symptoms affect the rhythms or patterns of heart
movement and are identifiable by analyzing ECG signal waveforms
100. Although the correlation between a given type of illness and
the ECG signal waveforms 100 is usually identifiable, the given
type of illness is more readily identifiable from resulting signals
after performing a data analysis on ECG signal waveforms 100
particularly for emphasizing the correlation between a given
illness type and the resulting signals.
[0021] FIG. 2 is a flow chart of various example methods of
analyzing the ECG signal. The process of performing an analysis on
the ECG signal in order to generate resultant data is also referred
to as feature extraction. In some embodiments, the disclosed
analyses are performed by a computer system or an ECG equipment
executing a software program, i.e., a set of
executable/interpretable instructions. It is understood that in
some embodiments only some of the disclosed analyses are performed
for analyzing ECG signals for a patient. In some embodiments, a
person having ordinary skill in the art will appreciate that
additional operations are performed before, during, and/or after
the method of FIG. 2.
[0022] In operation 202, an ECG signal such as the example signal
depicted by waveform 100 in FIG. 1 is obtained through ECG
transducers or stored ECG data transmitted from a storage device or
via a network. Then, in operation 204, the cardiac cycles C of the
ECG signal waveform 100 are identified. In some embodiments, the
identification of cardiac cycles C includes first detecting some of
the feature points. For example, in at least one embodiment, the
cardiac cycles C of the ECG signal waveform 100 are identified
according to detection of R-points 134 in the ECG signal waveform
100.
[0023] In some embodiments, at least one time-domain analysis 210
is performed on the ECG signal waveform 100. In at least one
embodiment, after detection of R-points 134, the time intervals
between two adjacent R-points (RR interval or RRI) T.sub.RR (FIG.
1) are calculated in operation 211. The RR intervals T.sub.RR
represent the periods of cardiac cycles C (also referred to as NN
intervals), i.e., the inverse information of a heart rate. Then, a
statistical analysis is performed on the calculated RR intervals
T.sub.RR in operation 212. In some embodiments, the calculated RR
intervals T.sub.RR are analyzed according to one or more of the
following approaches: standard deviation of NN intervals (SDNN),
standard deviation of average NN intervals (SDANN), the
root-mean-square of successive differences of RR intervals (RMSSD),
the number of pairs of successive NN intervals that differ by more
than 50 ms (NN50), the proportion of NN50 divided by total number
of NN intervals (pNN50), etc.
[0024] In at least another embodiment, after detection of R-points
134, other feature points and sections, such as Q-points 132,
S-points 136, P-wave 110, and T-wave 120, are also detected in
operation 213. Then, PR intervals T.sub.PR (FIG. 1) and QT
intervals T.sub.QT (FIG. 1) are calculated in operation 214, and
either one or both of these two intervals are informative features
for representing characteristics of a cardiac cycle C. Finally, in
operation 215, a statistical analysis similar to the methods
described above for operation 212 is performed on the calculated PR
intervals T.sub.PR, and the QT intervals T.sub.QT.
[0025] In some embodiments, at least one morphology analysis 230 is
performed on the ECG signal waveform 100. A morphology analysis
refers to an analysis based on the waveforms or patterns of the
examined signal. In at least one embodiment, after detection of
R-points 134, other feature points and sections, such as Q-points
132, S-points 136, P-wave 110, and T-wave 120, are also detected
and identified in operation 232. Subsequently in operation 234,
features such as slopes of ST segment 150, T-wave 120, P-wave 110,
or other features are derived from the variance of patterns of
recorded cardiac cycles C.
[0026] In at least one embodiment, in operation 236, the morphology
analysis 230 includes extracting features derived from the variance
between adjacent cardiac cycles C of the ECG signal waveform 100.
In at least another embodiment, the morphology analysis 230
performed in operation 238 includes a method of evaluating
morphology variance between two adjacent cardiac cycles C of the
ECG signal waveform 100 by calculating mutual information based on
joint and marginal probabilities.
[0027] In some embodiments, one or more other types of analyses 240
are performed on the ECG signal waveform 100. For example, in some
embodiments after detection of R-points 134, features concerning
electrical axis of a heart are calculated in operation 242 based on
positive/negative waves at feature points (such as P-wave 110,
Q-point 132, R-point 134, S-point 136, T-wave 120, etc.) from ECG
signals received from a different combination of leads.
[0028] FIG. 3 is a flow chart of a method of analyzing periodic
signals representing movement of a physical object according to
some embodiments. The analysis method depicted in FIG. 3 is a
method based upon morphology of the examined substantially periodic
signal, such as the ECG signal waveform 100 analyzed according to
operation 238 in FIG. 2. It is understood that in some embodiments
additional operations are performed before, during, and/or after
the method of FIG. 3.
[0029] In operation 310, a predetermined portion of the
substantially periodic signal corresponding to a predetermined time
period is segmented into a plurality of signal segments, each
signal segment including a nominal waveform. For example, in at
least one embodiment for which a predetermined duration of the ECG
signal waveform 100 is being analyzed, the nominal waveform is the
waveform pattern corresponding to a cardiac cycle C, and the
segmentation is performed by dissecting the ECG signal waveform 100
in between adjacent R-points. In some embodiments, the nominal
waveform is the waveform defined between adjacent R-points 134, and
the segmentation is performed by dissecting the ECG signal waveform
100 at R-points 134.
[0030] The analyzed substantially periodic signal, such as an ECG
signal waveform 100 in some embodiments, is a discrete-time signal
and thus the signal segments are also sequences of data points or
data arrays. However, because the effective frequency of the
analyzed substantially periodic signal varies even within the time
period of the predetermined portion of the substantially periodic
signal, the size of the signal segments is not necessarily the
same. Therefore, in operation 320, two signal arrays in adjacent
signal segments having the same size are obtained by re-sampling
one or both of the adjacent signal segments. "Re-sampling" refers
to re-creation of a continuous waveform based on an original data
array and deriving a new data array from the re-created continuous
waveform, and thus the new data array and the original data array
both represent the same continuous waveform. In some embodiments,
the re-sampling is performed by interpolation or extrapolation of
the original data array in a linear or polynomial manner or other
applicable curve-fitting algorithms.
[0031] For example, in some embodiments, one of two signal segments
corresponding to neighboring cardiac cycles C are re-sampled in
order to generate two corresponding signal arrays having the same
size. If, for example, after segmentation, a first cardiac cycle C
includes 262 data points, and a second cardiac cycle C includes 274
data points, either one of the first cardiac cycle or the second
cardiac cycle is re-sampled to match the size, i.e., same number of
data points, of the other cardiac cycle. In some embodiments, the
second cardiac cycle is re-sampled to generate a signal array
having the same size as the first cardiac cycle, e.g., said 262
data points.
[0032] In operation 330, after the re-sampling for adjacent signal
segments the joint probability and the marginal probability of the
two signal arrays of the adjacent signal segments are calculated.
In some embodiments, the joint probability and marginal probability
are calculated according to the two re-sampled signal arrays X and
Y of ECG signals of adjacent periods (i.e., adjacent signal
segments corresponding to neighboring cardiac cycles C). Elements
of the signal array X or signal array Y represent magnitudes of the
ECG signals. The marginal probability of each signal array (X or Y)
is determined by first accumulating the counts of each different
value of elements in the signal array, and then calculating the
proportion of each cumulative count to the total amount of elements
in the signal array to obtain the marginal probabilities of each
different value of elements p(x) or p(y). Considering both
sequences X and Y together, similar to the calculation of marginal
probability, the joint probability p(x,y) of two events x and y in
conjunction is determined. Then, in operation 340, a mutual
information index is calculated based on the calculated joint
probability and the calculated marginal probability of every
adjacent signal segments. In at least one embodiment, the
calculation of the mutual information index is performed based on
application of the following equation:
MI ( X ; Y ) = x y p ( x , y ) log p ( x , y ) p ( x ) p ( y )
##EQU00001##
X and Y represent components of one of the two signal arrays of the
adjacent signal segments, p(x,y) represents the joint probability
of the two signal arrays, and p(x) and p(y) each represents the
marginal probability of one of the two signal arrays of the
adjacent signal segments.
[0033] In operation 350, a mutual information array is generated
based on the calculated mutual information indexes. For example,
the mutual information array includes an array of the calculated
mutual information indexes listed based on their sequence in the
examined ECG signal waveform 100. The generated mutual information
array is usable for further information processing. In at least one
embodiment, the generated mutual information array is compared with
a predetermined benchmark pattern corresponding to one or more
predetermined types of illness in order to determine the likelihood
of the one or more corresponding predetermined types of
illness.
[0034] Although the method depicted in FIG. 3 is explained using
ECG signals as an example, a person having ordinary skill in the
art will appreciate that the same analysis method is usable for
analyzing substantially periodic signals other than ECG signals. In
some embodiments, the substantially periodic signal to be analyzed
is obtained by detecting one of the following activities:
heartbeat, breathing, speech, earthquake or seismic activity,
orbital movement of an astronomical object, periodic variances of
an astronomical object, movement of a piston, or rotation of a
motor. Also, the analysis method of FIG. 3 is usable to detect a
likelihood of a predetermined type of abnormal movement of the
physical object by comparing the mutual information matrix with a
predetermined benchmark pattern corresponding to the predetermined
type of abnormal movement.
[0035] FIG. 4 is a flow chart of a method of detecting abnormal
movement of a physical object according to some embodiments. The
method depicted in FIG. 4 is explained in the context of analyzing
an ECG signal waveform 100 as depicted in FIG. 1. It is understood
that, in some embodiments, the method depicted in FIG. 4 is usable
for analyzing various types of periodic signals representing
movement of a physical object. A person having ordinary skill in
the art will appreciate that in some embodiments additional
operations are performed before, during, and/or after the method of
FIG. 4.
[0036] In general, the method depicted in FIG. 4 incorporates
results from two or more different analyses or feature extraction
methods, such as the ones disclosed in FIG. 2, into an integrated
feature matrix in order to consolidate information for further
analysis, such as risk assessment based on an ECG signal or
abnormal movement detection of a physical object, while reducing
the total amount of data.
[0037] In operation 410, a predetermined portion of a substantially
periodic signal is obtained and pre-processed. In at least one
embodiment for analyzing an ECG signal, a predetermined period of
ECG signals is obtained by an ECG transducer to record electrical
potential difference of heart muscle cells caused by electrical
pulses of a heart that is being observed. In some embodiments, more
ECG transducers or the same transducer with leads attached to
different portions of a human body are used to provide electrical
axis information or other information. In yet some other
embodiments where other biological or non-biological signals are to
be analyzed, applicable detecting systems or transducers other than
ECG transducers are used.
[0038] In some embodiments, the detected ECG signal is further
amplified and/or level-shifted to have signal levels of the ECG
signal adjusted to be within a predetermined range for further
analog-to-digital conversion and/or filtering. In at least one
embodiment, the detected ECG signals are also affected by breathing
or factors not relevant to the heart activities. Therefore, the
contribution of noise or other component in the detected ECG signal
from irrelevant factors is suppressed in order to obtain a filtered
ECG signal, such as the example ECG signal depicted in FIG. 1, for
subsequent information processing.
[0039] After obtaining the predetermined portion of the
substantially periodic signal to be analyzed, a raw matrix
comprising at least two data arrays derived based on different
signal analyses is generated.
[0040] For example, in operation 420, a first analysis is performed
on the predetermined portion of the substantially periodic signal
to generate a first array, and a second analysis different from the
first analysis is performed on the predetermined portion of the
substantially periodic signal to generate a second array. In some
embodiments, more than two different analyses are performed and
more than two resulting data arrays are generated.
[0041] In some embodiments analyzing a non-ECG signal, the first
analysis or the second analysis is a time-domain analysis, a
morphology analysis, a pattern analysis, or derivation of a feature
from the predetermined duration of the substantially periodic
signal obtained according to a first spatial measurement
configuration and at least a portion of another substantially
periodic signal is representative of the movement obtained
according to a second spatial measurement configuration.
[0042] In some other embodiments analyzing an ECG signal waveform
100, the first analysis or the second analysis is a time-domain
analysis, a morphology analysis, an electric-axis analysis. For
example, two or more of the following analysis are performed: RR
Interval analysis (e.g., operations 211/212), PR-QT interval
analysis (e.g., operation 213/214/215), morphology feature analysis
(e.g., operation 232/234), morphology distance analysis (e.g.,
operation 236), mutual information analysis (e.g., method depicted
in FIG. 3), electric axis analysis (e.g., operation 242), or other
available ECG signal analyses.
[0043] In operation 430, a raw matrix is generated according to the
results of the analysis performed on the periodic signal, such as
the first analysis and the second analysis selected from one of the
ECG signal analysis methods depicted in FIGS. 2 and 3, as well as
other applicable ECG or biology signal analysis methods. In some
embodiments, results from a third or more different analyses are
incorporated in the raw matrix.
[0044] Subsequently in operation 440, an integrated feature matrix
having a dimension no greater than the dimension of the raw matrix
is generated by performing a dimension reduction on the raw matrix.
In some embodiments, the dimension reduction is performed by
applying principal component analysis, factor analysis, or
independent component analysis on the raw matrix.
[0045] In operation 450, the likelihood of a predetermined type of
abnormal movement of the physical object is determined by comparing
the integrated matrix with a predetermined benchmark pattern
corresponding to the predetermined type of abnormal movement. For
example, if ECG signals of a heart are analyzed, the abnormal
activities of the heart being examined are identified, and the
likelihood of a predetermined type of illness correlated to the
abnormal activities is assessed accordingly.
[0046] Although the method depicted in FIG. 4 is explained with
reference to analyzing ECG signals, the same analysis method is
usable for analyzing substantially periodic signal other than ECG
signals. In some embodiments, the substantially periodic signal to
be analyzed is obtained by detecting one of the following
activities: heartbeat, breathing, speech, earthquake or seismic
activity, orbital movement of an astronomical object, periodic
variances of an astronomical object, movement of a piston, or
rotation of a motor. Also, the analysis method of FIG. 4 is usable
to detect likelihood of a predetermined type of abnormal movement
of the physical object by comparing the integrated feature matrix
with a predetermined benchmark pattern corresponding to the
predetermined type of abnormal movement.
[0047] For example, in some embodiments that analyze a
predetermined duration of speech for recognizing the acoustic
characters of the speech, the predetermined duration of speech
signal is segmented into a plurality of windows, and each window
includes one or more periods of speech waveforms. The mutual
information analysis method of FIG. 3 is usable to obtain a mutual
information array representing the variations among different
windows. Further, together with data arrays derived by using other
analysis methods such as the ones similar to the methods depicted
in blocks 210/230/240 of FIG. 2, an integrated matrix is derived
using the method of FIG. 4 in order to suppress noises for
subsequent speech processing.
[0048] FIG. 5 is a functional block diagram of an abnormality
analysis system usable for implementing the method disclosed in
FIGS. 2-4 according to some embodiments.
[0049] Abnormality analysis system 500 includes a computer system
510 comprising a computer readable storage medium 512 encoded with,
i.e., storing, a computer program code, i.e., a set of executable
instructions. The computer system 510 includes a processor 514
electrically coupled to the computer readable storage medium 512.
The processor 514 is configured to execute or interpret the
computer program code encoded in the computer readable storage
medium 512 in order to cause the computer to function as a signal
analyzer for performing the abnormality analysis and risk
assessment for the substantially periodic signal to be examined,
such as an ECG signal, as depicted in FIGS. 2-4.
[0050] In some embodiments, the processor 514 is a central
processing unit (CPU), a multi-processor, a distributed processing
system, and/or any suitable processing unit. In at least one
embodiment, the processor 514 acquires information such as the
predetermined duration of periodic signal, the predetermined
benchmark pattern, and/or other information from the memory storage
medium 512.
[0051] In some embodiments, the computer readable storage medium
512 is an electronic, magnetic, optical, electromagnetic, infrared,
and/or a semiconductor system (or apparatus or device). For
example, the computer readable storage medium 512 includes a
semiconductor or solid-state memory, a magnetic tape, a removable
computer diskette, a random access memory (RAM), a read-only memory
(ROM), a rigid magnetic disk, and/or an optical disk. In some
embodiments using optical disks, the computer readable storage
medium 512 includes a compact disk-read only memory (CD-ROM), a
compact disk-read/write (CD-R/W), and/or a digital video disc
(DVD).
[0052] Further, the computer system 510 includes an input/output
interface 516 and a display 518. The input/output interface 516 is
coupled to the processor 514 and allows an operator or a medical
care professional to operate the computer system 510 in order to
perform the methods depicted in FIGS. 2-4. The display 518 displays
the status of operation of the methods depicted in FIGS. 2-4 in a
real-time manner, and preferably provides a Graphical User
Interface (GUI). The input/output interface 516 and the display 518
allow an operator to operate the computer system 512 in an
interactive manner.
[0053] The computer system 510 also includes a network interface
522 coupled to the processor 514. The network interface 522 allows
the computer system 510 to communicate with a network 530, to which
one or more other computer systems are connected. The network
interface 522 includes wireless network interfaces such as
BLUETOOTH, WIFI, WIMAX, GPRS, or WCDMA; or wired network interface
such as ETHERNET, USB, or IEEE-1394. In some embodiments, the
method of FIGS. 2-4 are implemented in two or more computer systems
510 of FIG. 5, and information such as the predetermined duration
of periodic signal, the predetermined benchmark pattern, and/or
other information are exchanged between different computer systems
via the network 530.
[0054] In at least one embodiment, the abnormality analysis system
500 further comprises a transducer 540. The transducer 540 is
capable of observing the physical object to be examined and
converting the movement of the physical object into a
representative signal. In some embodiments analyzing ECG signals,
the transducer 540 observes the heart to be examined and converts
the muscle movement of the heart into ECG signals.
[0055] The computer system 510 further has an interface 524 coupled
to the transducer 540 and the processor 514. The interface 524
bridges the transducer 540 with the processor 514 and outputs the
picked up periodic signals in discrete-time signal format. For
example, if the transducer 540 picks up an ECG signal, the
interface receives the ECG signal from the transducer 540 and
outputs the ECG signal in the format of a ECG data array to the
processor 514. In some embodiments, the transducer 540 converts one
of the following physical phenomenon into electrical signals:
heartbeats, breathing, speech, earthquakes, orbital movement of an
astronomical object, periodic variances of an astronomical object,
movement of a piston, or rotation of a motor.
[0056] FIG. 6A is a chart of resulting data arrays 612/614 of two
different analyses performed on a benchmark ECG signal representing
abnormal movement of a heart, an integrated matrix 616 according to
the results of the two different analyses, and an annotated
waveform 618 identifying windows 622.about.628 for pulses
corresponding to abnormal movement according to some
embodiments.
[0057] The example depicted in FIG. 6A uses at least two different
ECG feature extraction methods: time-domain RR Interval analysis
and morphology distance analysis. The testing is performed based on
arrhythmia database of Massachusetts Institute of Technology and
Boston's Beth Israel Hospital (MIT-BIH), which is an international
standard database. Comparing the result of RR Interval analysis 612
with the annotated waveform 618, application of the RR Interval
analysis on the examined ECG signal reveals abnormal movement
corresponding to the abnormality identified at windows 622 and 628.
However, the result of RR Interval analysis 612 fails to identify
the abnormal movement corresponding to the abnormality identified
at windows 624 and 626. In the same example, the result of
morphology distance analysis 614, compared with the annotated
waveform 618, reveals abnormal movement corresponding to the
abnormality identified at windows 626. However, the result of
morphology distance analysis 614 fails to identify the abnormal
movement corresponding to the abnormality identified at windows
622, 624, and 628.
[0058] After information integration and dimension reduction in
accordance with the method depicted in FIG. 4, the information
relevant to identifying abnormal movement corresponding to the
abnormality identified at windows 622, 626, and 628 from resulting
arrays 612 and 614 are integrated into the integrated feature
matrix 616. In at least one embodiment, the integrated feature
matrix 616 is a one-by-N array derived from a raw matrix, which is
a two-by-N matrix including two one-by-N arrays (the resulting
arrays 612 and 614). N is the number of data points in the
resulting arrays 612 and 614.
[0059] Therefore, a single integrated feature matrix 616 is usable
for identifying the abnormality identifiable by the resulting
arrays 612 and 614. That is, the integrated matrix 616 integrates
and preserves information in the resulting arrays 612 and 614
relevant to subsequent abnormality detection while reducing the
overall volume of information, and thus to improve the computation
efficiency in subsequent determination of abnormal movement.
[0060] FIG. 6B is a chart of resulting data arrays 612/614/632 of
three different analyses performed on a benchmark ECG signal
representing abnormal movement of a heart, an integrated matrix 634
according to the results of the three different analyses, and an
annotated waveform 618 identifying windows 622.about.628 for pulses
corresponding to abnormal movement according to some embodiments.
The testing is also performed based on the MIT-BIH arrhythmia
database. In addition to the results of RR Interval analysis 612
and morphology distance analysis, the resulting data array 632 is
derived based on mutual information analysis as depicted in FIG. 3.
Compared with the annotated waveform 618, applying mutual
information analysis reveals abnormal movement corresponding to the
abnormality identified at windows 622 and 628, and also at window
624, which is not easily detectable from solely the results of RR
Interval analysis 612 and morphology distance analysis 614.
Although, the result of mutual information analysis 632 fails to
identify the abnormal movement corresponding to window 626, mutual
information analysis helps to remedy the deficiencies of the RR
Interval analysis and the morphology distance analysis.
[0061] After information integration and dimension reduction as
depicted in FIG. 4, the information relevant to identifying
positions 622, 624, 626, and 628 from resulting arrays 612, 614,
and 632 are integrated into the integrated feature matrix 634. In
at least one embodiment, the integrated feature matrix 634 is a
one-by-N array derived from a raw matrix, which is a three-by-N
matrix including three one-by-N arrays (the results 612, 614, and
632). N being the number of data points in the resulting data
arrays 612, 614, and 632. Therefore, a single integrated feature
matrix 634 is usable for identifying the abnormal movement
identifiable by the resulting arrays 612, 614, and 632 by using a
reduced-size matrix. That is, the integrated feature matrix 634
integrates and preserves information relevant to subsequent
abnormality detection while reducing the overall volume of
information, and thus to improve the computation efficiency in
subsequent determination of abnormal movement.
[0062] FIG. 7A is a chart of resulting data arrays of performing
asymmetric index (Al) of multi-scale analysis on results of RR
interval analysis of ECG signals obtained from observing End Stage
Renal Disease (ESRD) patients, with or without diabetes mellitus
(DM), according to some configurations. It is known to the
applicants that ESRD patients' conditions regarding DM are
discernable by Glycated hemoglobin (HbA1c) tests. While ESRD
patients with DM demonstrate HbA1c greater than 5.7, ESRD patients
without DM demonstrate HbA1c less than 5.7. As depicted in FIG. 7A,
results of performing the Al analysis based on results of RR
interval analysis for ESRD patients with DM 712 and ESRD patients
without DM 714 are not helpful in distinguishing these two
different groups.
[0063] FIG. 7B is a chart of resulting data arrays of performing Al
of multi-scale analysis on an integrated feature matrix based on
results of RR interval analysis, morphology distance analysis and
mutual information analysis of ECG signals obtained from observing
ESRD patients, with or without DM, according to some embodiments.
As depicted in FIG. 7B, results of performing the Al based on
results of integrated matrix for ESRD patients with DM 722 and
without DM 724 fall in different ranges, and thus the analysis is
helpful in distinguishing these two different groups. Thus,
compared with performing analysis depicted in FIG. 2 individually,
in some embodiments, the analysis method as depicted in FIG. 4 not
only incorporates information from various analyses, but also
enhances the correlation between the results of the analysis, i.e.,
the integrated feature matrix, and one or more patterns
corresponding to particular illness or abnormality. Moreover,
compared with tests that require blood-drawing and days of
laboratory test, analyzing ECG signals is a relatively less
invasive and more time-efficient approach in distinguishing ESRD
patients with and without DM.
[0064] The foregoing outlines features of several embodiments so
that those skilled in the art may better understand the aspects of
the present disclosure. Those skilled in the art should appreciate
that they may readily use the present disclosure as a basis for
designing or modifying other processes and structures for carrying
out the same purposes and/or achieving the same advantages of the
embodiments introduced herein. Those skilled in the art should also
realize that such equivalent constructions do not depart from the
spirit and scope of the present disclosure, and that they may make
various changes, substitutions, and alterations herein without
departing from the spirit and scope of the present disclosure.
* * * * *