U.S. patent application number 11/601773 was filed with the patent office on 2007-03-22 for method and device for analyzing a periodic or semi-periodic signal.
This patent application is currently assigned to BSP Biological Signal Processing Ltd.. Invention is credited to Amir Beker, Tamir Ben-David, Assaf Hasson.
Application Number | 20070066907 11/601773 |
Document ID | / |
Family ID | 11073657 |
Filed Date | 2007-03-22 |
United States Patent
Application |
20070066907 |
Kind Code |
A1 |
Beker; Amir ; et
al. |
March 22, 2007 |
Method and device for analyzing a periodic or semi-periodic
signal
Abstract
A device for reducing noise in signals having successive
substantially repetitive portions, comprising: an iterative
averager operative to superimpose and average said substantially
repetitive portions to produce a running average thereof, and an
iteration ender comprising a noise analyzer for determining a noise
level in said running average and ending operation of said
iterative averager when said noise level reaches a predetermined
level. Also, A method of obtaining an indication of ischemia in a
patient using an ECG signal therefrom, the method comprising:
extracting an ECG signal over a duration, extracting from said ECG
signal a series of QRS complexes over said duration, extracting
high frequency components of said QRS complexes, analyzing said
high frequency components over said duration for at least one of a
predetermined quality, and inferring from said predetermined
quality an indication of ischemia.
Inventors: |
Beker; Amir; (Rosh HaAyin,
IL) ; Hasson; Assaf; (Tel-Aviv, IL) ;
Ben-David; Tamir; (Tel-Aviv, IL) |
Correspondence
Address: |
Martin D. Moynihan;PRTSI, Inc.
P.O. Box 16446
Arlington
VA
22215
US
|
Assignee: |
BSP Biological Signal Processing
Ltd.
Tel-Aviv
IL
|
Family ID: |
11073657 |
Appl. No.: |
11/601773 |
Filed: |
November 20, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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10168673 |
Jun 25, 2002 |
7151957 |
|
|
PCT/IL00/00871 |
Dec 28, 2000 |
|
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11601773 |
Nov 20, 2006 |
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Current U.S.
Class: |
600/509 |
Current CPC
Class: |
A61B 5/366 20210101 |
Class at
Publication: |
600/509 |
International
Class: |
A61B 5/04 20060101
A61B005/04 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 29, 1999 |
IL |
133780 |
Claims
1. A device for reducing noise in signals having successive
substantially repetitive portions, comprising: an iterative
averager operative to superimpose and average said substantially
repetitive portions to produce a running average thereof, and an
iteration ender comprising a noise analyzer for determining a noise
level in said running average and ending operation of said
iterative averager when said noise level reaches a predetermined
level.
2. A device for reducing noise in signals according to claim 1,
wherein said iterative averager is operative to take said
successive portions in successive iterative steps.
3. A device for reducing noise in signals according to claim 1,
further comprising an aligner for aligning at least some of said
substantially repetitive portions one with another, wherein said
signal comprises first frequency and second frequency components
and said aligner comprises a first frequency correlated band pass
filter and a second frequency correlated band pass filter to
extract respective first and second frequency components, thereby
to use said first frequency components to locate an alignment point
in successive portions and to use said alignment point to align
said second frequency components.
4. A device for reducing noise in signals according to claim 1,
wherein said iteration ender is further operative to end said
operation of said iterative averager when said repetitive portions
are exhausted.
5. A device for reducing noise in signals according to claim 1,
wherein said iteration ender is further operative to end said
operation of said iterative averager when said running average
reaches a preset maximum of included repetitive portions.
6. A device for reducing noise in signals according to claim 1,
further comprising a repetitive portion selector for selecting
repetitive portions for passing to said iterative averager, the
repetitive portion selector comprising a reference portion store
for storing a reference portion, a cross correlator for computing a
cross correlation between a current repetitive portion and said
reference portion, and a comparator for comparing a result of said
cross-correlation with a predetermined threshold to produce a
comparison output, and wherein said selector is operable to pass
said current repetitive portion to said iterative averager in
accordance with said comparison output.
7. A device for reducing noise in signals according to claim 6,
further comprising a reference portion determination unit
associated with said repetitive portion selector, operable to
determine as a reference portion any one of a group comprising a
first repetitive portion of a current length of said signal, a
final result of a running average of a previous set of iterations
and a prior determined typical wave.
8. A device for reducing noise in signals according to claim 7,
wherein said reference portion determination unit is operable to
dynamically change between members of said group over the course of
a set of iterations.
9. A device for reducing noise in signals according to claim 7,
wherein said reference portion determination unit further comprises
a reference portion updater for dynamically updating said reference
portion during the course of a set of iterations.
10. A device for reducing noise in signals according to claim 6,
comprising a reference portion determiner, said reference portion
determiner comprising, a first store for storing a first set of
repetitive portions from said signal, a second store for storing a
second set of repetitive portions from said signal, a cross
correlator for cross-correlating repetitive portions from said
second set in turn with repetitive portions from said first set to
produce a plurality of cross-correlation results for respective
repetitive portions in said second set, and a reference selector
for selecting one of said repetitive portions in said second set as
a reference portion in accordance with its respective
cross-correlation results.
11. A device for noise reduction in a signal according to claim 10,
wherein said reference selector comprises a threshold level
comparator for comparing each cross-correlation result with a
threshold and which is operable to select as said reference portion
a repetitive portion having a highest number of respective
cross-correlation results exceeding said threshold.
12. A device for noise reduction according to claim 10, wherein
said reference selector comprises a summation unit for summing
cross-correlation results of respective repetitive portions and
which reference selector is operable to select as a reference
portion a repetitive portion having the highest sum of respective
cross-correlation results.
13. A device for noise reduction according to claim 10, wherein
said reference selector comprises: a threshold level comparator for
comparing each cross-correlation result with a threshold, and a
summation unit for summing cross-correlation results of respective
repetitive portions exceeding said threshold, and which reference
selector is operable to select as a reference portion a repetitive
portion having a highest sum of respective cross-correlation
results.
14. A device for noise reduction in a signal according to claim 1,
further comprising a signal extractor for extracting said
repetitive portion.
15. A device for noise reduction in a signal according to claim 14
comprising an RMS computation unit for calculation of the energy
level of segments of wave obtained by averaging a series of said
repetitive portions.
16. A device for noise reduction in a signal according to claim 15,
further comprising an RMS value analysis unit for detecting a
falloff in said RMS energy value over succeeding averages.
17. A device for noise reduction in a signal according to claim 14,
comprising a cross-correlation unit for computing the cross
correlation coefficient of an average of a series of said
repetitive portions and a reference wave.
18. A device for noise reduction in a signal according to claim 17,
further comprising a cross-correlation value analysis unit for
detecting a falloff in said cross-correlation value over succeeding
averages.
19. A device for noise reduction in a signal according to claim 3,
wherein said aligner further comprises a cross-correlator for
cross-correlating a current input with said running average at a
plurality of successive alignments and for aligning said signal on
the basis of an alignment giving a maximum cross-correlation.
20. A device for noise reduction in a signal according to claim 19
wherein said aligner further comprises: an interpolator for
interpolating between said cross-correlations at said successive
alignments to determine a higher accuracy sub-sample alignment, and
a wave shifter for shifting said current input in accordance with
said determined sub-sample alignment.
21. A device for reducing noise in signals having successive
substantially repetitive portions, comprising: an aligner for
aligning at least some of said substantially repetitive portions
one with another, a repetitive portion selector for selecting
repetitive portions for passing to said iterative averager on the
basis of a comparison with a reference portion, and an iterative
averager operative to superimpose and average said aligned,
selected portions to produce a running average thereof.
22. A device for reducing noise in signals according to claim 21,
further comprising a repetitive portion selector for selecting
repetitive portions for passing to said iterative averager, the
repetitive portion selector comprising a reference portion store
for storing a reference portion, a cross correlator for computing
the cross correlation between a current repetitive portion and said
reference portion, and a comparator for comparing a result of said
cross-correlation with a predetermined threshold to produce a
comparison output, and wherein said selector is operable to pass
said current repetitive portion to said iterative averager in
accordance with said comparison output.
23. A device for reducing noise in signals according to claim 21,
further comprising a reference portion determination unit
associated with said repetitive portion selector, operable to
determine as a reference portion any one of a group comprising a
first repetitive portion of a current length of said signal, a
final result of a running average of a previous set of iterations
and a prior determined typical wave.
24. A device for reducing noise in signals according to claim 22,
wherein said reference portion determination unit is operable to
dynamically change between members of said group over a course of a
set of iterations.
25. A device for reducing noise in signals according to claim 22,
wherein said reference portion determination unit further comprises
a reference portion updater for dynamically updating said reference
portion during a course of a set of iterations.
26. A device for reducing noise in signals according to claim 21,
comprising a reference portion determiner, said reference portion
determiner comprising, a first store for storing a first set of
repetitive portions from said signal, a second store for storing a
second set of repetitive portions from said signal, a
cross-correlator for cross-correlating repetitive portions from
said second set in turn with repetitive portions from said first
set to produce a plurality of cross-correlation results for
respective repetitive portions in said second set, and a reference
selector for selecting one of said repetitive portions in said
second set as a reference portion in accordance with its respective
cross-correlation results.
27. A device for noise reduction in a signal according to claim 25,
wherein said reference selector comprises a threshold level
comparator for comparing each cross-correlation result with a
threshold and which is operable to select as said reference portion
a repetitive portion having a highest number of respective
cross-correlation results exceeding said threshold.
28. A device for noise reduction according to claim 25, wherein
said reference selector comprises a summation unit for summing
cross-correlation results of respective repetitive portions and
which reference selector is operable to select as a reference
portion a repetitive portion having a highest sum of respective
cross-correlation results.
29. A device for noise reduction according to claim 25, wherein
said reference selector comprises: a threshold level comparator for
comparing each cross-correlation result with a threshold, and a
summation unit for summing cross-correlation results of respective
repetitive portions exceeding said threshold, and which reference
selector is operable to select as a reference portion a repetitive
portion having a highest sum of respective cross-correlation
results.
30. A waveform frequency component alignment device for aligning
first frequency components of waveforms having first frequency and
second frequency components, the device comprising: Band pass
filters for extracting respective first and second frequency
components of said waveform, a first frequency component aligner
for determining a first frequency alignment point of a current
waveform with another waveform based on respective first frequency
components, and a second frequency aligner for aligning said second
frequency components of said respective waveforms based on said
first frequency alignment point.
31. A waveform frequency component alignment device according to
claim 30, wherein said other waveform is a running average of
preceding waveforms.
32. A waveform frequency component alignment device according to
claim 30, wherein said first frequency component aligner further
comprises a cross-correlator for cross-correlating a current
waveform with said other waveform at a plurality of successive
alignments and for determining said first frequency alignment point
on the basis of a one of said successive alignments giving a
maximum cross-correlation.
33. A waveform high frequency component alignment device according
to claim 32, wherein said first frequency component aligner further
comprises an interpolator for interpolating between said
cross-correlations at said successive alignments to determine a
sub-sample accuracy alignment point between said successive
alignments.
34. A device for analyzing high frequency components of ECG
signals, comprising a data extractor for extracting said high
frequency components and a data analyzer for determining, from a
change over time in at least a part of said high frequency
component, whether said ECG signal contains an indication of the
presence of ischemia.
35. A device for analyzing high frequency components of ECG signals
according to claim 34, wherein said at least a part of said ECG
signal is a QRS complex.
36. A device according to claim 35, wherein said change over time
is a fall in the energy level of succeeding QRS complexes.
37. A device according to claim 35, wherein said change over time
is a fall in a cross-correlation value of succeeding QRS
complexes.
38. A device according to claim 34, wherein said data extractor
comprises a waveform averager for performing iterative averaging
over successive ones of said high frequency components to obtain a
reduced noise version of said components.
39. A device according to claim 38, further comprising a selector
for passing to said waveform averager only those ones of said
successive components which exceed a threshold cross-correlation
with a reference component.
40. A method for reducing noise in signals having successive
substantially repetitive portions, comprising: superimposing one by
one weightwise in iterative steps weighted instances of at least
some of said successive substantially repetitive portions, forming
a running average of said portions, determining a noise level in
said running average, and ending said iterative steps when said
noise level reaches a predetermined level, thereby to produce an
average of said substantially repetitive portions having reduced
noise.
41. A method for reducing noise in signals according to claim 40,
further comprising a step of aligning at least some of said
substantially repetitive portions one with another,
42. A method for reducing noise in signals according to claim 41,
wherein said signal comprises first frequency and second frequency
components and said step of aligning comprises substeps of
extracting said respective first and second frequency components,
using said first frequency components to locate an alignment point
in each of successive portions, and using said alignment point to
align said second frequency components of each of said successive
portions.
43. A method for reducing noise in signals according to claim 40,
wherein said step of ending said iterative steps further comprises
ending when said repetitive portions are exhausted.
44. A method for reducing noise in signals according to claim 40,
wherein said step of ending said iterative steps further comprises
ending when said running average reaches a preset maximum of
included repetitive portions.
45. A method for reducing noise in signals according to claim 40,
further comprising the step of selecting repetitive portions for
passing to said iterative averager, the step of repetitive portion
selecting comprising substeps of: storing a reference portion,
Computing the cross correlation between a current repetitive
portion and said reference portion, comparing a result of said
cross-correlation with a predetermined threshold to produce a
comparison output, and passing said current repetitive portion for
iterative averaging in accordance with said comparison output.
46. A method for reducing noise in signals according to claim 45,
further comprising the step of determining as a reference portion
any one of a group comprising a first repetitive portion of a
current length of said signal, a final result of a running average
of a previous set of iterations and a prior determined typical
wave.
47. A method for reducing noise in signals according to claim 46,
wherein said step of selecting comprises the further substep of
dynamically changing between members of said group over the course
of a set of iterations.
48. A method for reducing noise in signals according to claim 46,
wherein said step of selecting further comprises dynamically
updating said reference portion during the course of a set of
iterations.
49. A method for reducing noise in signals according to claim 45,
comprising a step of determining a reference portion by: storing a
first set of repetitive portions from said signal, storing a second
set of repetitive portions from said signal, cross-correlating
repetitive portions from said second set in turn with repetitive
portions from said first set to produce a plurality of
cross-correlation results for respective repetitive portions in
said second set, and selecting one of said repetitive portions in
said second set as a reference portion in accordance with its
respective cross-correlation results.
50. A method for noise reduction in a signal according to claim 49,
further comprising: comparing each cross-correlation result with a
threshold, and selecting as said reference portion a repetitive
portion having a highest number of respective cross-correlation
results exceeding said threshold.
51. A method for noise reduction according to claim 49, wherein
said step of determining a reference further comprises: summing
cross-correlation results of respective repetitive portions and
selecting as a reference portion a repetitive portion having the
highest sum of respective cross-correlation results.
52. A method for noise reduction according to claim 49, wherein
said step of determining a reference portion further comprises:
comparing each cross-correlation result with a threshold, summing
cross-correlation results of respective repetitive portions
exceeding said threshold, and selecting as a reference portion a
repetitive portion having a highest sum of respective
cross-correlation results.
53. A method for noise reduction in a signal according to claim 40,
further comprising the step of extracting QRS complexes from an ECG
signal to provide said repetitive portion.
54. A method for noise reduction in a signal according to claim 40
comprising extracting an RMS energy value from an average of a
series of said repetitive portions.
55. A method for noise reduction in a signal according to claim 54,
further comprising a step of analyzing said RMS energy to detect
for the presence of a falloff in said RMS energy value over
succeeding averages.
56. A method for noise reduction in a signal according to claim 40,
comprising extracting a cross-correlation value from an average of
a series of said repetitive portions.
57. A method for noise reduction in a signal according to claim 56,
further comprising the step of analyzing succeeding ones of said
cross correlation value to detect the presence of a falloff in said
cross-correlation value over succeeding averages.
58. A method for noise reduction in a signal according to claim 42,
wherein said alignment step further comprises cross-correlating a
current input with said running average at a plurality of
successive alignments, and aligning said signal on the basis of an
alignment giving a maximum cross-correlation.
59. A method for noise reduction in a signal according to claim 58,
said step of alignment further comprising interpolating between
said cross-correlations at successive alignments to determine a
high accuracy alignment between said successive alignments.
60. A method for reducing noise in signals having successive
substantially repetitive portions, comprising: aligning at least
some of said substantially repetitive portions one with another,
selecting repetitive portions for passing to said iterative
averager on the basis of a comparison with a reference portion, and
superimposing and averaging said aligned, selected portions to
produce a running average thereof.
61. A method for reducing noise in signals according to claim 60,
further comprising a step of selecting from said repetitive
portions for passing to said iterative averager, the step of
selecting comprising substeps of: storing a reference portion,
carrying out a cross correlation between a current repetitive
portion and said reference portion, comparing a result of said
cross-correlation with a predetermined threshold to produce a
comparison output, and passing said current repetitive portion to
said iterative averager in accordance with said comparison
output.
62. A method for reducing noise in signals according to claim 60,
comprising the further step of selecting as a reference portion any
one of a group comprising a first repetitive portion of a current
length of said signal, a final result of a running average of a
previous set of iterations and a prior determined typical wave.
63. A method for reducing noise in signals according to claim 62,
wherein said step of selecting a reference portion includes a
substep of dynamically change between members of said group over
the course of a set of iterations.
64. A device for reducing noise in signals according to claim 62,
wherein said step of selecting a reference portion includes
dynamically updating said reference portion during the course of a
set of iterations.
65. A method for reducing noise in signals according to claim 61,
comprising a step of determining a reference point, said step
comprising, storing a first set of repetitive portions from said
signal, storing a second set of repetitive portions from said
signal, cross-correlating repetitive portions from said second set
in turn with repetitive portions from said first set to produce a
plurality of cross-correlation results for respective repetitive
portions in said second set, and selecting one of said repetitive
portions in said second set as a reference portion in accordance
with its respective cross-correlation results.
66. A method for noise reduction in a signal according to claim 65,
comprising the further steps of: comparing each cross-correlation
result with a threshold, and selecting as said reference portion a
repetitive portion having a highest number of respective
cross-correlation results exceeding said threshold.
67. A method for noise reduction according to claim 65, comprising
the further steps of summing cross-correlation results of
respective repetitive portions, and selecting as a reference
portion a repetitive portion having the highest sum of respective
cross-correlation results.
68. A method for noise reduction according to claim 65, comprising
the further steps of: comparing each cross-correlation result with
a threshold, summing cross-correlation results of respective
repetitive portions exceeding said threshold, and selecting as a
reference portion a repetitive portion having a highest sum of
respective cross-correlation results.
69. A method of aligning waveforms having first and second
frequency components, said second frequency components being more
subject to noise than said first frequency components, the method
comprising: extracting respective first and second frequency
components of said waveform, determining an alignment point of a
current waveform with another waveform based on respective first
frequency components, and aligning said second frequency components
of said respective waveforms based on said alignment point.
70. A method of aligning waveforms according to claim 69, wherein
said other waveform is a running average of preceding
waveforms.
71. A method of aligning waveforms according to claim 69,
comprising the further steps of: cross-correlating a current
waveform with said other waveform at a plurality of successive
alignments, and determining said alignment point on the basis of a
one of said successive alignments giving a maximum
cross-correlation.
72. A method of aligning waveforms according to claim 71,
comprising the further step of interpolating between said
cross-correlations at said successive alignments to obtain a
sub-sample alignment point.
73. A method for analyzing high frequency components of ECG
signals, comprising the steps of: extracting said high frequency
components and determining, from a change over time in at least a
part of said high frequency component, whether said ECG signal
contains an indication of the presence of ischemia.
74. A method for analyzing high frequency components of ECG
signals, according to claim 73, wherein said at least a part of
said ECG signal is at least part of a QRS complex.
75. A method for analyzing high frequency components of an ECG
signal according to claim 74, wherein said change over time is a
fall in an RMS energy level of succeeding QRS complexes.
76. A method for analyzing high frequency components of an ECG
signal according to claim 74, wherein said change over time is a
fall in a cross-correlation value of succeeding QRS complexes.
77. A method for analyzing high frequency components of an ECG
signal according to claim 75, comprising the further step of
performing iterative averaging over successive ones of said high
frequency components to obtain a reduced noise version of said
components.
78. A method for analyzing high frequency components of an ECG
signal according to claim 77, further comprising a selection step
of comparing successive waveforms with a reference component and
selecting only those ones of said successive waveforms which
exceeds a threshold cross-correlation level with said reference
component for said step of iterative averaging.
79. A method of obtaining an indication of ischemia in a patient
using an ECG signal therefrom, the method comprising: extracting an
ECG signal over a duration, extracting from said ECG signal a
series of at least partial QRS complexes over said duration,
extracting high frequency components of said QRS complexes,
analyzing said high frequency components over said duration for at
least one of a predetermined quality, and inferring from said
predetermined quality an indication of ischemia.
80. A method according to claim 79, wherein said predetermined
quality is a falloff in a cross-correlation level with a reference
component.
81. A method according to claim 79, wherein said predetermined
quality is a falloff in the energy level of said component.
82. A method according to claim 79, wherein said step of extracting
said high frequency components comprises carrying out iterative
steps of averaging over preselected ones of successive components
to reduce noise
83. A method according to claim 79, wherein said step of extracting
an ECG signal is carried out over a duration of a stress test
comprising placing the patient in at least one of a group of phases
comprising rest, stress, and recovery from stress.
84. A method according to claim 79, wherein said step of extracting
an ECG signal is carried out over a duration of an event being any
one of a group comprising: acute myocardial ischemia, other forms
of heart failure, coronary occlusion, and coronary angioplasty,
said duration being any one of a group comprising before, during
and after said event.
85. A method according to claim 79, wherein said ECG signal is
masked by another ECG signal.
86. A method of producing a noise reduced waveform from a series of
substantially similar repeated waveforms having superimposed noise,
the method comprising: selecting waveforms having a highest
cross-correlation with a preselected reference waveform, and
carrying out iterative averaging steps using said selected
waveforms.
87. A method according to claim 83 comprising the further step of
ending said iterative averaging when a signal to noise ratio of a
result of said iterative averaging has a level below a
predetermined threshold.
Description
RELATED APPLICATIONS
[0001] This Application is a Divisional of U.S. patent application
Ser. No. 10/168,673, filed on Jun. 25, 2002, which is a U.S.
National Phase of PCT Patent Application PCT/IL00/00871, filed on
Dec. 28, 2000, which claims priority of Israel Patent Application
No. IL133780, filed Dec. 29, 1999.
FIELD AND BACKGROUND OF THE INVENTION
[0002] The present invention relates to a method and device for
analyzing a periodic or semiperiodic signal and more particularly
but not exclusively to analyzing a signal having the form of a
typical electrocardiograph signal, again more particularly but not
exclusively to obtaining an improved signal to noise ratio from
such a signal.
[0003] The electrocardiograph (ECG) signal describes the electrical
activity of the cardiac muscle as it generates the various stages
of the heart wave. Each cycle in the ECG signal may be subdivided
into segments corresponding to stages of the heart wave, such as
the P wave, the QRS complex, the T wave, the ST segment etc. Thus,
the P wave of the ECG signal is due to depolarization of the atria,
the QRS complex to depolarization of the ventricles, and the T wave
to repolarization of the ventricles. Detection of an altered ECG
signal is an important non-invasive tool in the diagnosis of
cardiac abnormalities. Analysis of the ECG signal usually focuses
on the ST segment due to its low-noise and its well-known
correlation with cardiac abnormalities such as coronary artery
disease (CAD). As physical stress is known to introduce features
into the ECG signal indicative of CAD not present in signals
obtained at rest it is common to obtain an ECG signal from a
subject during a stress test comprising phases of rest, exercise
and recovery from exercise.
[0004] In order to obtain sufficient information to provide useful
diagnostic information from ECG measurements it is thus current
practice to obtain data under the above stress test. It is
desirable, moreover, to maximize the amount of information that can
be obtained on line, for example when a patient arrives in
intensive care.
[0005] The significant frequency range of the ECG signal is
traditionally considered to be in the range of 0-100 Hz. However,
valuable information is also known to exist in ECG components
having frequencies above this range. For example, low amplitude
components above 100 Hz at the end of the QRS complex, known as
late potentials, correlate with delayed activation of the heart
wave; decreased energy levels of the high frequency components in
the mid-QRS portion of the signal are associated with ischemia.
However, these high frequency components have very low amplitude
and are usually masked by the noise at this frequency. Thus, a
simple band-pass filter will not be enough for the detection of
such phenomena, and more elaborate signal processing techniques
should be used to enhance the signal-to-noise ratio.
[0006] U.S. Pat. No. 4,732,158, to Sadeh discloses a process for
averaging ECG cycles over a time period to produce an average or
representative cycle for the time period having decreased noise in
the QRS segment. By the process of this patent, an ECG signal is
obtained and band passed filtered. At each stage of the process, a
new wave is averaged with the wave that was obtained in the
previous stage if its maximum cross-correlation coefficient with a
given reference wave exceeds a predetermined value. The averaging
process is terminated when a predetermined number (e.g., about 100,
or more) of successive ECG signal waves have been averaged. This
averaging procedure, however, incorporates several significant
drawbacks:
[0007] The improvement of the signal to noise ratio at the output
of the process is very limited. More precisely, the noise level of
the averaged signal at the output of the process can, at best,
reach half the original noise level.
[0008] The averaging process tends to attenuate transient changes
that may be of interest for the analysis of the signal.
[0009] The method is insensitive to the variability of the noise
level in the signal.
[0010] A high level of alignment is a prerequisite for any
effective averaging process. Thus, filtering the signal, before
performing the alignment, as suggested in the above method, makes
it practically inapplicable to signals, such as the HF component of
ECG, where the signal to noise ratio in the frequencies of interest
is very high.
[0011] Thus the methods known in the art are not effective in the
analysis of signals with a varying level of relatively high noise,
especially when transient changes in the signals are of importance.
A typical example of such a signal is the HF ECG component obtained
during the above-mentioned exercise test, which has been shown to
be of importance in early detection of ischemia--it has been shown
by Beker et al. (Proceedings on computers in Cardiology, IEEE
Computer Society, 33-35,1992) and Beker et al. (Pacing and Clinical
electrophysiology 12:2040, 1996) that decrease in the energy level
of the HF component of the QRS interval during the course of the
exercise test may be indicative of ischemia.
[0012] There is therefore a need for a method to enhance the
signal-to-noise ratio in periodic or semi-periodic signals, such as
ECG signals--a method that will eliminate or substantially reduce
the disadvantages of the prior art methods.
SUMMARY OF THE INVENTION
[0013] Embodiments of the present invention provide a process,
referred to herein as "adaptive averaging", for effecting an
improvement in the signal-to-noise ratio of periodic or
semi-periodic signals. Preferred embodiments comprise the following
features: [0014] 1. Effective noise reduction, without any a-priori
limit on the improvement of the signal to noise ratio. [0015] 2.
Reduced attenuation of transient changes in the signal, and [0016]
3. Dynamic response to changes in the noise level of the
signal.
[0017] According to a first aspect of the present invention there
is thus provided a device for reducing noise in signals having
successive substantially repetitive portions, comprising:
[0018] an iterative averager operative to superimpose and average
said substantially repetitive portions to produce a running average
thereof,
[0019] and an iteration ender comprising a noise analyzer for
determining a noise level in said running average and ending
operation of said iterative averager when said noise level reaches
a predetermined level.
[0020] Preferably, said iterative averager is operative to take
said successive portions in successive iterative steps.
[0021] Preferably, the device further comprises an aligner for
aligning at least some of said substantially repetitive portions
one with another, wherein said signal comprises first frequency and
second frequency components and said aligner comprises a first
frequency correlated band pass filter and a second frequency
correlated band pass filter to extract respective first and second
frequency components, thereby to use said first frequency
components to locate an alignment point in successive portions and
to use said alignment point to align said second frequency
components.
[0022] Preferably, said iteration ender is further operative to end
said operation of said iterative averager when said repetitive
portions are exhausted.
[0023] Preferably, said iteration ender is further operative to end
said operation of said iterative averager when said running average
reaches a preset maximum of included repetitive portions.
[0024] Preferably, the device further comprises a repetitive
portion selector for selecting repetitive portions for passing to
said iterative averager, the repetitive portion selector
comprising
[0025] a reference portion store for storing a reference
portion,
[0026] a cross correlator for computing a cross correlation between
a current repetitive portion and said reference portion, and
[0027] a comparator for comparing a result of said
cross-correlation with a predetermined threshold to produce a
comparison output,
[0028] and wherein said selector is operable to pass said current
repetitive portion to said iterative averager in accordance with
said comparison output.
[0029] Preferably, the device further comprises a reference portion
determination unit associated with said repetitive portion
selector, operable to determine as a reference portion any one of a
group comprising a first repetitive portion of a current length of
said signal, a final result of a running average of a previous set
of iterations and a prior determined typical wave.
[0030] Preferably, said reference portion determination unit is
operable to dynamically change between members of said group over
the course of a set of iterations.
[0031] Preferably, said reference portion determination unit
further comprises a reference portion updater for dynamically
updating said reference portion during the course of a set of
iterations.
[0032] Preferably, the device further comprises a reference portion
determiner, said reference portion determiner comprising,
[0033] a first store for storing a first set of repetitive portions
from said signal,
[0034] a second store for storing a second set of repetitive
portions from said signal,
[0035] a cross correlator for cross-correlating repetitive portions
from said second set in turn with repetitive portions from said
first set to produce a plurality of cross-correlation results for
respective repetitive portions in said second set,
[0036] and a reference selector for selecting one of said
repetitive portions in said second set as a reference portion in
accordance with its respective cross-correlation results.
[0037] Preferably, said reference selector comprises a threshold
level comparator for comparing each cross-correlation result with a
threshold and which is operable to select as said reference portion
a repetitive portion having a highest number of respective
cross-correlation results exceeding said threshold.
[0038] Preferably, the device further comprises a summation unit
for summing cross-correlation results of respective repetitive
portions and which reference selector is operable to select as a
reference portion a repetitive portion having the highest sum of
respective cross-correlation results.
[0039] Preferably, said reference selector comprises:
[0040] a threshold level comparator for comparing each
cross-correlation result with a threshold, and
[0041] a summation unit for summing cross-correlation results of
respective repetitive portions exceeding said threshold,
[0042] and which reference selector is operable to select as a
reference portion a repetitive portion having a highest sum of
respective cross-correlation results.
[0043] Preferably, the device further comprises a signal extractor
for extracting said repetitive portion.
[0044] Preferably, the device further comprises an RMS computation
unit for calculation of the energy level of segments of wave
obtained by averaging a series of said repetitive portions.
[0045] Preferably, the device further comprises an RMS value
analysis unit for detecting a falloff in said RMS energy value over
succeeding averages.
[0046] Preferably, the device further comprises a cross-correlation
unit for computing the cross correlation coefficient of an average
of a series of said repetitive portions and a reference wave.
[0047] Preferably, the device further comprises a cross-correlation
value analysis unit for detecting a falloff in said
cross-correlation value over succeeding averages.
[0048] Preferably, said aligner further comprises a
cross-correlator for cross-correlating a current input with said
running average at a plurality of successive alignments and for
aligning said signal on the basis of an alignment giving a maximum
cross-correlation.
[0049] Preferably, said aligner further comprises:
[0050] an interpolator for interpolating between said
cross-correlations at said successive alignments to determine a
higher accuracy sub-sample alignment, and a wave shifter for
shifting said current input in accordance with said determined
sub-sample alignment.
[0051] According to a second aspect of the present invention, there
is provided a device for reducing noise in signals having
successive substantially repetitive portions, comprising:
[0052] an aligner for aligning at least some of said substantially
repetitive portions one with another,
[0053] a repetitive portion selector for selecting repetitive
portions for passing to said iterative averager on the basis of a
comparison with a reference portion, and
[0054] an iterative averager operative to superimpose and average
said aligned, selected portions to produce a running average
thereof.
[0055] Preferably, the device further comprises a repetitive
portion selector for selecting repetitive portions for passing to
said iterative averager, the repetitive portion selector
comprising
[0056] a reference portion store for storing a reference
portion,
[0057] a cross correlator for computing the cross correlation
between a current repetitive portion and said reference portion,
and
[0058] a comparator for comparing a result of said
cross-correlation with a predetermined threshold to produce a
comparison output,
[0059] and wherein said selector is operable to pass said current
repetitive portion to said iterative averager in accordance with
said comparison output.
[0060] Preferably, the device further comprises a reference portion
determination unit associated with said repetitive portion
selector, operable to determine as a reference portion any one of a
group comprising a first repetitive portion of a current length of
said signal, a final result of a running average of a previous set
of iterations and a prior determined typical wave.
[0061] Preferably, said reference portion determination unit is
operable to dynamically change between members of said group over a
course of a set of iterations.
[0062] Preferably, said reference portion determination unit
further comprises a reference portion updater for dynamically
updating said reference portion during a course of a set of
iterations.
[0063] Preferably, the device further comprises a reference portion
determiner, said reference portion determiner comprising,
[0064] a first store for storing a first set of repetitive portions
from said signal,
[0065] a second store for storing a second set of repetitive
portions from said signal,
[0066] a cross-correlator for cross-correlating repetitive portions
from said second set in turn with repetitive portions from said
first set to produce a plurality of cross-correlation results for
respective repetitive portions in said second set,
[0067] and a reference selector for selecting one of said
repetitive portions in said second set as a reference portion in
accordance with its respective cross-correlation results.
[0068] Preferably, said reference selector comprises a threshold
level comparator for comparing each cross-correlation result with a
threshold and which is operable to select as said reference portion
a repetitive portion having a highest number of respective
cross-correlation results exceeding said threshold.
[0069] Preferably, the device further comprises a summation unit
for summing cross-correlation results of respective repetitive
portions and which reference selector is operable to select as a
reference portion a repetitive portion having a highest sum of
respective cross-correlation results.
[0070] Preferably, said reference selector comprises:
[0071] a threshold level comparator for comparing each
cross-correlation result with a threshold, and
[0072] a summation unit for summing cross-correlation results of
respective repetitive portions exceeding said threshold,
[0073] and which reference selector is operable to select as a
reference portion a repetitive portion having a highest sum of
respective cross-correlation results.
[0074] According to a third aspect of the present invention there
is provided a waveform frequency component alignment device for
aligning first frequency components of waveforms having first
frequency and second frequency components, the device
comprising:
[0075] band pass filters for extracting respective first and second
frequency components of said waveform,
[0076] a first frequency component aligner for determining a first
frequency alignment point of a current waveform with another
waveform based on respective first frequency components,
[0077] and a second frequency aligner for aligning said second
frequency components of said respective waveforms based on said
first frequency alignment point.
[0078] Preferably, said other waveform is a running average of
preceding waveforms.
[0079] Preferably, said first frequency component aligner further
comprises a cross-correlator for cross-correlating a current
waveform with said other waveform at a plurality of successive
alignments and for determining said first frequency alignment point
on the basis of a one of said successive alignments giving a
maximum cross-correlation.
[0080] Preferably, said first frequency component aligner further
comprises an interpolator for interpolating between said
cross-correlations at said successive alignments to determine a
sub-sample accuracy alignment point between said successive
alignments.
[0081] According to a fourth aspect of the present invention there
is provided a device for analyzing high frequency components of ECG
signals, comprising
[0082] a data extractor for extracting said high frequency
components and
[0083] a data analyzer for determining, from a change over time in
at least a part of said high frequency component, whether said ECG
signal contains an indication of the presence of ischemia.
[0084] Preferably, said at least a part of said ECG signal is a QRS
complex.
[0085] Preferably, said change over time is a fall in the energy
level of succeeding QRS complexes.
[0086] Preferably, said change over time is a fall in a
cross-correlation value of succeeding QRS complexes.
[0087] Preferably, said data extractor comprises a waveform
averager for performing iterative averaging over successive ones of
said high frequency components to obtain a reduced noise version of
said components.
[0088] Preferably, the device further comprises a selector for
passing to said waveform averager only those ones of said
successive components which exceeds a threshold cross-correlation
with a reference component.
[0089] According to a fifth aspect of the present invention there
is provided a method for reducing noise in signals having
successive substantially repetitive portions, comprising:
[0090] superimposing one by one weightwise in iterative steps
weighted instances of at least some of said successive
substantially repetitive portions,
[0091] forming a running average of said portions,
[0092] determining a noise level in said running average, and
[0093] ending said iterative steps when said noise level reaches a
predetermined level, thereby to produce an average of said
substantially repetitive portions having reduced noise.
[0094] The method preferably further comprises a step of aligning
at least some of said substantially repetitive portions one with
another,
[0095] Preferably, said signal comprises first frequency and second
frequency components and said step of aligning comprises substeps
of
[0096] extracting said respective first and second frequency
components,
[0097] using said first frequency components to locate an alignment
point in each of successive portions, and
[0098] using said alignment point to align said second frequency
components of each of said successive portions.
[0099] Preferably, said step of ending said iterative steps further
comprises ending when said repetitive portions are exhausted.
[0100] Preferably, said step of ending said iterative steps further
comprises ending when said running average reaches a preset maximum
of included repetitive portions.
[0101] The method preferably further comprises the step of
selecting repetitive portions for passing to said iterative
averager, the step of repetitive portion selecting comprising
substeps of:
[0102] storing a reference portion,
[0103] Computing the cross correlation between a current repetitive
portion and said reference portion,
[0104] comparing a result of said cross-correlation with a
predetermined threshold to produce a comparison output, and
[0105] passing said current repetitive portion for iterative
averaging in accordance with said comparison output.
[0106] The method preferably further comprises the step of
determining as a reference portion any one of a group comprising a
first repetitive portion of a current length of said signal, a
final result of a running average of a previous set of iterations
and a prior determined typical wave.
[0107] Preferably, said step of selecting comprises the further
substep of dynamically changing between members of said group over
the course of a set of iterations.
[0108] Preferably, said step of selecting further comprises
dynamically updating said reference portion during the course of a
set of iterations.
[0109] The method preferably further comprises a step of
determining a reference portion by:
[0110] storing a first set of repetitive portions from said
signal,
[0111] storing a second set of repetitive portions from said
signal,
[0112] cross-correlating repetitive portions from said second set
in turn with repetitive portions from said first set to produce a
plurality of cross-correlation results for respective repetitive
portions in said second set, and
[0113] selecting one of said repetitive portions in said second set
as a reference portion in accordance with its respective
cross-correlation results.
[0114] The method preferably further comprises:
[0115] comparing each cross-correlation result with a threshold,
and
[0116] selecting as said reference portion a repetitive portion
having a highest number of respective cross-correlation results
exceeding said threshold.
[0117] Preferably, said step of determining a reference further
comprises:
[0118] summing cross-correlation results of respective repetitive
portions and
[0119] selecting as a reference portion a repetitive portion having
the highest sum of respective cross-correlation results.
[0120] Preferably, said step of determining a reference portion
further comprises:
[0121] comparing each cross-correlation result with a
threshold,
[0122] summing cross-correlation results of respective repetitive
portions exceeding said threshold, and
[0123] selecting as a reference portion a repetitive portion having
a highest sum of respective cross-correlation results.
[0124] The method preferably further comprises the step of
extracting QRS complexes from an ECG signal to provide said
repetitive portion.
[0125] The method preferably further comprises extracting an RMS
energy value from an average of a series of said repetitive
portions.
[0126] The method preferably further comprises a step of analyzing
said RMS energy to detect for the presence of a falloff in said RMS
energy value over succeeding averages.
[0127] The method preferably further comprises extracting a
cross-correlation value from an average of a series of said
repetitive portions.
[0128] The method preferably further comprises the step of
analyzing succeeding ones of said cross correlation value to detect
the presence of a falloff in said cross-correlation value over
succeeding averages.
[0129] Preferably, said alignment step further comprises
cross-correlating a current input with said running average at a
plurality of successive alignments, and
[0130] aligning said signal on the basis of an alignment giving a
maximum cross-correlation.
[0131] The step of alignment preferably further comprises further
comprising interpolating between said cross-correlations at
successive alignments to determine a high accuracy alignment
between said successive alignments.
[0132] According to a sixth aspect of the present invention there
is provided a method for reducing noise in signals having
successive substantially repetitive portions, comprising:
[0133] aligning at least some of said substantially repetitive
portions one with another,
[0134] selecting repetitive portions for passing to said iterative
averager on the basis of a comparison with a reference portion,
and
[0135] superimposing and averaging said aligned, selected portions
to produce a running average thereof.
[0136] The method preferably further comprises a step of selecting
from said repetitive portions for passing to said iterative
averager, the step of selecting comprising substeps of:
[0137] storing a reference portion,
[0138] carrying out a cross correlation between a current
repetitive portion and said reference portion,
[0139] comparing a result of said cross-correlation with a
predetermined threshold to produce a comparison output, and
[0140] passing said current repetitive portion to said iterative
averager in accordance with said comparison output.
[0141] The method preferably further comprises the step of
selecting as a reference portion any one of a group comprising a
first repetitive portion of a current length of said signal, a
final result of a running average of a previous set of iterations
and a prior determined typical wave.
[0142] Preferably, said step of selecting a reference portion
includes a substep of dynamically change between members of said
group over the course of a set of iterations.
[0143] Preferably, said step of selecting a reference portion
includes dynamically updating said reference portion during the
course of a set of iterations.
[0144] The method preferably further comprises a step of
determining a reference point, said step comprising,
[0145] storing a first set of repetitive portions from said
signal,
[0146] storing a second set of repetitive portions from said
signal,
[0147] cross-correlating repetitive portions from said second set
in turn with repetitive portions from said first set to produce a
plurality of cross-correlation results for respective repetitive
portions in said second set, and
[0148] selecting one of said repetitive portions in said second set
as a reference portion in accordance with its respective
cross-correlation results.
[0149] The method preferably further comprises the further steps
of:
[0150] comparing each cross-correlation result with a threshold,
and
[0151] selecting as said reference portion a repetitive portion
having a highest number of respective cross-correlation results
exceeding said threshold.
[0152] The method preferably further comprises the further steps
of
[0153] summing cross-correlation results of respective repetitive
portions, and
[0154] selecting as a reference portion a repetitive portion having
the highest sum of respective cross-correlation results.
[0155] The method preferably comprises the further steps of:
[0156] comparing each cross-correlation result with a
threshold,
[0157] summing cross-correlation results of respective repetitive
portions exceeding said threshold, and
[0158] selecting as a reference portion a repetitive portion having
a highest sum of respective cross-correlation results.
[0159] According to a seventh aspect of the present invention there
is provided a method of aligning waveforms having first and second
frequency components, said second frequency components being more
subject to noise than said first frequency components, the method
comprising:
[0160] extracting respective first and second frequency components
of said waveform,
[0161] determining an alignment point of a current waveform with
another waveform based on respective first frequency components,
and
[0162] aligning said second frequency components of said respective
waveforms based on said alignment point.
[0163] Preferably, said other waveform is a running average of
preceding waveforms.
[0164] The method preferably further comprises the further steps
of:
[0165] cross-correlating a current waveform with said other
waveform at a plurality of successive alignments, and
[0166] determining said alignment point on the basis of a one of
said successive alignments giving a maximum cross-correlation.
[0167] The method preferably comprises the further step of
interpolating between said cross-correlations at said successive
alignments to obtain a sub-sample alignment point.
[0168] According to an eighth aspect of the present invention there
is provided a method for analyzing high frequency components of ECG
signals, comprising the steps of:
[0169] extracting said high frequency components and
[0170] determining, from a change over time in at least a part of
said high frequency component, whether said ECG signal contains an
indication of the presence of ischemia.
[0171] Preferably, said at least a part of said ECG signal is at
least part of a QRS complex.
[0172] Preferably, said change over time is a fall in an RMS energy
level of succeeding QRS complexes.
[0173] Preferably, said change over time is a fall in a
cross-correlation value of succeeding QRS complexes.
[0174] The method preferably further comprises the further step of
performing iterative averaging over successive ones of said high
frequency components to obtain a reduced noise version of said
components.
[0175] The method preferably further comprises a selection step of
comparing successive waveforms with a reference component and
selecting only those ones of said successive waveforms which
exceeds a threshold cross-correlation level with said reference
component for said step of iterative averaging.
[0176] According to a ninth aspect of the present invention there
is provided a method of obtaining an indication of ischemia in a
patient using an ECG signal therefrom, the method comprising:
[0177] extracting an ECG signal over a duration,
[0178] extracting from said ECG signal a series of at least partial
QRS complexes over said duration,
[0179] extracting high frequency components of said QRS
complexes,
[0180] analyzing said high frequency components over said duration
for at least one of a predetermined quality, and
[0181] inferring from said predetermined quality an indication of
ischemia.
[0182] Preferably, said predetermined quality is a falloff in a
cross-correlation level with a reference component.
[0183] Preferably, said predetermined quality is a falloff in the
energy level of said component.
[0184] Preferably, said step of extracting said high frequency
components comprises carrying out iterative steps of averaging over
preselected ones of successive components to reduce noise
[0185] Preferably, said step of extracting an ECG signal is carried
out over a duration of a stress test comprising placing the patient
in at least one of a group of phases comprising rest, stress, and
recovery from stress.
[0186] Preferably, said step of extracting an ECG signal is carried
out over a duration of an event being any one of a group
comprising: acute myocardial ischemia, other forms of heart
failure, coronary occlusion, and coronary angioplasty, said
duration being any one of a group comprising before, during and
after said event.
[0187] Preferably, said ECG signal is masked by another ECG
signal.
[0188] According to a tenth aspect of the presetninvention there is
provided a method of producing a noise reduced waveform from a
series of substantially similar repeated waveforms having
superimposed noise, the method comprising:
[0189] selecting waveforms having a highest cross-correlation with
a preselected reference waveform, and
[0190] carrying out iterative averaging steps using said selected
waveforms.
[0191] The method preferably further comprises the step of ending
said iterative averaging when a signal to noise ratio of a result
of said iterative averaging has a level below a predetermined
threshold.
BRIEF DESCRIPTION OF THE DRAWINGS
[0192] For a better understanding of the invention and to show how
the same may be carried into effect, reference will now be made,
purely by way of example, to the accompanying drawings.
[0193] With specific reference now to the drawings in detail, it is
stressed that the particulars shown are by way of example and for
purposes of illustrative discussion of the preferred embodiments of
the present invention only, and are presented in the cause of
providing what is believed to be the most useful and readily
understood description of the principles and conceptual aspects of
the invention. In this regard, no attempt is made to show
structural details of the invention in more detail than is
necessary for a fundamental understanding of the invention, the
description taken with the drawings making apparent to those
skilled in the art how the several forms of the invention may be
embodied in practice. In the accompanying drawings:
[0194] FIG. 1 is a generalized flow diagram showing a method of
signal averaging according to a first embodiment of the present
invention,
[0195] FIG. 2 is a generalized flow diagram showing in greater
detail the stage of selecting an individual wave by thresholding,
according to the embodiment of FIG. 1,
[0196] FIG. 3 is a generalized diagram showing how a newly
extracted wave is aligned with an existing set before averaging
according to the embodiment of FIG. 1,
[0197] FIG. 4 is a generalized diagram showing a preferred method
of obtaining a reference wave for use in the embodiment of FIG.
1,
[0198] FIG. 5 is a generalized block diagram showing the
application of the embodiment of FIG. 1 to a system for extracting
information from an ECG signal,
[0199] FIG. 6 is a generalized flow diagram illustrating an
alignment stage in adaptive averaging of an ECG signal, and
[0200] FIG. 7 is a generalized flow diagram showing noise reduction
in a wave set extracted from a signal such as an ECG signal.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0201] Before explaining at least one embodiment of the invention
in detail, it is to be understood that the invention is not limited
in its application to the details of construction and the
arrangement of the components set forth in the following
description or illustrated in the drawings. The invention is
applicable to other embodiments and of being practiced or carried
out in various ways. Also, it is to be understood that the
phraseology and terminology employed herein is for the purpose of
description and should not be regarded as limiting.
[0202] In this specification, terminology is used in accordance
with the following definitions: [0203] 1. "signal" refers to an
entire length of periodic or semi-periodic input. [0204] 2.
"segment" indicates an arbitrary part of the signal (usually, but
not necessarily, a continuous part of the signal). [0205] 3. "wave"
means a segment consisting of a single occurrence of the periodic
part of the signal. [0206] 4. calculable or measurable variables
are herein defined to be equivalent to each other if they are
substantially proportional to each other.
[0207] To simplify the following description it is assumed that,
unless explicitly stated otherwise, the adaptive averaging
procedure takes as its input:
[0208] a digitized signal,
[0209] a set of waves extracted from that signal and aligned using
any method known in the art, and
[0210] for each wave, pointers indicating its correct beginning and
end within the signal. The skilled person will appreciate that, in
order to arrange such inputs, a certain amount of pre-processing
using known systems will be required.
[0211] The output of the procedure is an array of waves, obtained
from the original set of waves by applying noise reduction to the
set. Preferably, the output for each waveset is an array of
"cleaned" waves. I.e. whenever the averaged wave resulting from
noise reduction reaches a satisfactory noise level the algorithm
does not stop but rather restarts the same procedure, either with
the next wave in the same waveset or with a new waveset, as
appropriate in the circumstances.
[0212] The noise reduction procedure of the preferred embodiments
is based on averaging sets of succeeding waves in an iterative
process. At each iteration of the adaptive averaging procedure a
new wave is incorporated into a set of waves created during the
previous stages and an average is calculated over the modified set.
As will be described below, an alternative embodiment stores at
each iteration only the averaged wave, and then obtains a new
averaged wave using a weighted averaging wherein, at the n.sup.th
iteration the averaged wave has weight (n-1) and the new wave has
weight 1.
[0213] Unlike the averaging procedure suggested in the above
mentioned prior art patent, in which each new wave is averaged with
the wave produced in the preceding step, embodiments of the present
invention are able, at least conceptually, to reduce the signal to
noise ratio to any desired level.
[0214] A preferred feature of the present embodiments is that they
permit a user to define a target signal to noise ratio. Such a
target may be either constant, or may dynamically change according
to a function, supplied by the user, taking as arguments such
parameters as the local noise level, the local signal amplitude
etc. The use of a selected target level is advantageous in that it
provides assurance that as soon as the selected target signal to
noise ratio has been reached no further processing takes place,
thus minimizing attenuating effects caused by surplus noise
reduction steps.
[0215] Thus, whenever the required signal to noise ratio level is
attained, the current averaged wave is stored in an array of
results, and the procedure is repeated with the next wave in the
queue. If the target level is not reached within a predetermined
number of iterations, the procedure stores the result after the
predetermined number of iterations and moves on to the next wave or
waveset in a new noise reduction procedure as described above.
[0216] In order to improve the averaging procedure and reduce
attenuation in the signal produced by the averaging, waves with
exceedingly high noise level are preferably identified so that they
are not incorporated into the average. Therefore, in a preferred
embodiment, a reference wave is used in order to rate the
waves--for example using the cross correlation coefficient between
the candidate input wave and the reference wave--such that only
those waves whose rating is above a predetermined level are
included in the average.
[0217] Reference is now made to FIG. 1, which is a generalized flow
diagram of a signal averaging procedure according to a first
preferred embodiment of the present invention, for enhancing a
signal to noise level of the signal to a predetermined level. The
predetermined level may be set as a function of a current noise
level in the signal or in any other way. It is desirable that the
predetermined level is not too exacting, as excessive noise
reduction tends to harm fine information structures in the signal.
On the other hand the level should be sufficiently high to render
the signal intelligible for analysis purposes later on. In FIG. 1,
in a first step 10, an input wave set is received from an external
source and stored in a buffer or other storage means. The input
wave set may be a segment of a signal and may comprise a plurality
of repeated portions (waves). This step is followed by a reference
definition step 12 in which a reference noise level is defined, as
will be explained in greater detail below, and this is followed by
a step 14 of taking a next wave of the set obtained in step 10.
Preferably, the "next wave" is the first wave--in temporal
order--not yet incorporated to the running set.
[0218] The next wave is judged against a series of criteria as will
be described in more detail below and, if it is found to qualify,
is added to a running set of waves taken from the current input
waveset.
[0219] In a step 16, an average is calculated of the running set of
waves, as updated by the next wave in step 14. In the case of the
first qualifying wave in the set the wave itself constitutes the
average. Generally, even though a waveform may vary over time,
waves that are close to one another vary very little and thus
averaging such closely related waves is an effective method of
removing noise whilst retaining as much as possible of the
essential structure of the waveform.
[0220] The step of averaging is repeated by taking succeeding waves
in the waveset, and thus carrying out an iteration of steps 14, and
16 until one of the following events occur:
[0221] 1) a reference low noise threshold is reached,
[0222] 2) the input waveset is exhausted of all waves, or
[0223] 3) a predetermined number of iterations has been
reached.
[0224] The reference low noise threshold is preferably calculated
based on the reference noise level defined in step 12 above.
[0225] The continuation or ending of the iterative process is
illustrated by process step 18, and such control of the iterative
process is preferred because it provides a means for ending the
noise reduction process before waveform structure is adversely
effected. To this end the reference noise threshold level and the
number of iterations are preferably both set to optimize for later
analysis of the waveform, which demands minimal noise but maximal
surviving waveform structure.
[0226] At the end of the iteration series of the current waveform
set, in step 20, the resulting average waves are stored for later
analysis. The original waves that have already been processed from
the current waveset set and the running waveset may now be deleted
if no longer required.
[0227] If the current waveset is not exhausted, the running set is
deleted, a next wave is chosen, and the process continues from step
14. The choice of the next wave may be the first wave in the
temporally arranged waveset that has not been processed. Another
way of choosing the next wave may be by choosing, for example the
second wave in the running set. Such a choice preferably ensures
that the new average is very close to the previous one, resulting
in a much smoother change in the array of averaged waves. When the
current waveset is exhausted, a new waveset is chosen for iterative
analysis, and the process returns to step 10. The wavesets selected
for analysis may be contiguous segments of the original signal.
Alternatively, they may be segments taken at suitable intervals in
accordance with an anticipated level of change in the signal.
[0228] In an alternative to the above, instead of storing the
running waveset and separately calculating an average over all the
waves therein at each iteration, it is also possible to calculate a
new average at each iteration by taking the old average and
weighting it according to the number of waves it is constructed of
and then adding the new wave with a weight of 1.
[0229] Reference is now made to FIG. 2, which is a simplified flow
diagram showing in more detail step 14 of FIG. 1 in which a next
wave of the input waveset is added to the running set if it
qualifies under a predetermined set of criteria. In a first step,
30, a reference wave is defined. The reference wave may be the
first wave of the current iteration, or it may be an average wave
of a previous iteration or it may be a typical wave determined from
theory or over a series of previous calculations on the same or
similar data. Any given system may use one or more of the above
sources in order to obtain a reference wave. For example, for a
first segment it may use data from theory. For a second segment it
may use the average from the first segment and for subsequent
segments it may use data averaged over the previous segments in
general.
[0230] In step 32, the wave currently being considered, the "next"
wave, of step 14, is preferably cross-correlated with the reference
wave and the cross-correlation is itself compared against a
predetermined threshold in step 34. Provided the threshold is
exceeded, then, in step 36 the result is added to the running set
as described above. If not then step 38 is entered and the wave is
discarded.
[0231] Reference is now made to FIG. 3, which is a simplified flow
diagram showing in more detail the step 36 of FIG. 2. As mentioned
above, individual next waves are extracted from the initially
stored waveset, here shown as step 40. The wave has preferably been
pre-processed with alignment markers so that different waves having
broadly the same waveform may be aligned. Details of the process of
introducing alignment markers will be discussed in greater detail
below. In step 42 the next wave is aligned, using the alignment
marker, with the waves in the running set of extracted waves. The
aligned wave is now added to the set in step 44 and, because all
the waves are aligned it is possible to compute a new average over
all the waves in the running set in step 46.
[0232] Referring back to FIG. 2, a number of possibilities were
mentioned for obtaining a reference wave and it has been shown how
a preferred embodiment uses such a reference wave to decide whether
to accept a next wave into the running set. Reference is now made
to FIG. 4 which is a generalized flow diagram showing a preferred
embodiment for obtaining a reference wave based on individual waves
from two wavesets. In FIG. 4 a first waveset is obtained from an
input signal and individual waves are stored in a step 50. Then, in
a step 52 a second set of waves are obtained and stored in the same
way. In step 54, successive waves are taken from the second set and
in a step 56 cross-correlation between each of the waves in the
second set and each of the waves in the first set are computed to
produce a cross-correlation result. Each cross-correlation result
is then tested against a threshold level in step 58 and then for
each wave in the second set a value k is assigned giving the number
of waves in the first set with which the respectively computed
cross-correlation coefficient exceeded the threshold. In step 60
the k value for each individual wave from the second set is tested
against a threshold, and provided that k exceeds a predetermined
threshold, the wave is added to a stored waveset in step 62.
Otherwise the wave is discarded in step 64.
[0233] The procedure preferably continues until all of the waves in
the second set have been processed, step 66, and then one of the
waves of the stored waveset is selected as the reference wave.
There are a number of possibilities for selecting a reference wave
from the stored waveset that may be considered by the skilled
person. One preferred possibility is to take the wave having the
highest sum of cross-correlations and another preferred possibility
is to select the wave having the highest k value as a reference
value.
[0234] There is thus described, in FIGS. 1 to 4, a system of
adaptive averaging which allows an input signal comprising a
repetitive waveform to be segmented and for a clean version of the
waveform to be produced for later analysis. The system is useful
for any input signal having a repetitive portion and unwanted noise
and is particularly useful when analysis is dependent on careful
preservation of fine structural portions of the signal.
[0235] Reference is now made to FIG. 5, which is a simplified block
diagram of a system for carrying out adaptive averaging prior to
analysis of an ECG signal. The system comprises a signal
acquisition unit 70, which obtains signals from an input source, in
this example, an ECG signal.
[0236] The signal is typically an analogue signal containing fine
structure, as will be described in further detail below and is
generally initially mixed with noise. As will be discussed in more
detail below, the noise forms a higher proportion of the overall
input at frequency bands of interest, such that other frequency
parts of the signal are generally usable directly, but the signal
at the desired frequency parts generally requires noise reduction.
However, important information carried in parts of the signal in
the frequency band of interest is liable to be distorted or lost by
conventional noise reduction techniques.
[0237] A signal amplification and digitization unit, 72, processes
the signal so that it can be supplied for digital signal
processing. The QRS complex part of the signal is then detected by
a QRS detection unit 74 and then an alignment unit 76 detects
individual waves and marks corresponding portions of the waves for
subsequent alignment.
[0238] The alignment unit is connected in series to noise reduction
unit 78, HF filtering unit 80 and fine alignment unit 82
respectively, the functions of which will be discussed below.
Finally a data extraction unit 84 extracts averaged waveforms for
subsequent data analysis.
[0239] In order to understand the embodiment of FIG. 5 in greater
detail, the following preliminary remarks are made regarding
analysis of HF ECG. As has been shown by Abboud et al., an ischemic
condition of the heart is highly correlated with significant
decrease of the HF ECG signal of the QRS complex. Further studies
by Beker et al. (see above) have shown that a decrease of the HF
ECG of the QRS complex during exercise test could serve as
diagnostic aid for early detection of cardiovascular infarction, in
a way that is much more sensitive than the standard ECG signal.
[0240] Enlarging on what was stated above concerning the need for
noise reduction, the HF ECG signal is typically weaker and thus
harder to detect in a meaningful manner than the standard ECC, and
therefore, cannot be usefully dealt with before significantly
improving the signal to noise ratio. However, even during a
comparatively long test such as an exercise test (typically 10-20
min. long) the decrease in the HF ECG level may quite often be
observed only in relatively short intervals (2-3 min.). Thus a
noise reduction method that requires averaging large number of
waves can significantly attenuate the effect for which it was
employed, which means that the decrease in the HF ECG signal is
barely noticeable in the noise reduced signal.
[0241] From the above preliminary remarks it is clear that the
analysis of HF ECG preferably requires a noise reduction method
that will not abolish transient changes, since the transient
changes are of great diagnostic significance in that signal. The
adaptive averaging method of the embodiments described above in
respect of FIGS. 1-4 preferably allows a significant reduction in
the noise level of the HF ECG, with a considerably weaker
attenuation effect on phenomena, particularly transient phenomena,
due to changes in the signal itself. FIG. 5 illustrates the general
outlines of a specific embodiment intended for the processing of an
HF ECG signal in such a way as to remove noise but to retain
transient information.
[0242] The block diagram of FIG. 5 preferably enables a procedure
which permits analysis of the HF signal of the QRS complex. Thus,
in the following discussion, the term "wave" corresponds to a
single QRS complex. More generally, the system may be used for an
analysis of the HF signal in any other significant part of an ECG
signal, to give similar output results.
[0243] It should be noted, moreover, that individual steps of the
iterations as outlined above do not generally deal with large
quantities of raw data at a time. Therefore, it is envisaged that
the skilled person will be able to provide additional memory and
other data handling facilities of the appropriate type as deemed
necessary in order to provide an on line implementation of the
technique.
[0244] Acquisition 70, amplification and digitization 72, of the
signal are preferably standard operations. Typically, the standard
ECG signal is obtained by placing electrodes on an individual's
skin. The signal is then amplified and digitized using any standard
signal amplifier and an A/D converter with an appropriate sampling
rate (i.e. greater than twice the highest frequency in the signal
to be analyzed). As a rule most of the important information
contained in the HF ECG signal is considered to be in the range of
100-500 Hz, calling for a sampling rate higher than 200 Hz, and in
one embodiment 1000 Hz.
[0245] The QRS complex detector 74 is preferably implemented with a
standard QRS detection algorithm. One preferred algorithm consists
of locating all local maxima (or minima, depending on the signal's
polarization) of the overall signal that have a value higher (or
respectively lower) than a predetermined value corresponding to an
expected level of the QRS amplitude. Another preferred algorithm
consists of creating a template QRS complex (for example, by
manually selecting one of the waves in the raw data), cross
correlating it with the digitized signal and obtaining a cross
correlation function. Local maxima of the cross-correlation
function having a value exceeding a predetermined threshold may
correspond to QRS complexes of the ECG signal (provided the
threshold used is high enough).
[0246] Following the QRS complex detector 74 is a QRS alignment
stage 76. In stage 76 a procedure is implemented for marking QRS
complexes so that they may be aligned and then aligning them. In
order to perform an alignment procedure it is preferable that the
complexes are free of any DC components. Preferably, any such DC
components are removed at least by the time that QRS component
detection has been completed, either by rejecting the DC of the raw
signal before performing the detection and extraction of the waves,
or by performing QRS detection using the raw signal but extracting
the waves themselves from a signal that has already been
high-pass-filtered.
[0247] It is noted that from a practical point of view, the
alignment unit 76 and the noise reduction unit 78 of the system of
FIG. 5 may be merged and implemented as a single unit, although
from the conceptual viewpoint they are distinct. Thus, in the
present discussion units 76 and 78 will be described separately,
although in practice the skilled person would probably see fit to
implement them as a single unit carrying out both functions
together.
[0248] In order to simplify the implementation of the alignment
unit 76, it is preferable to assume that the waves extracted from
the signal around each maximum point of the cross correlation
function share substantially the same length. However, such an
assumption is very much dependent on the circumstances of
individual embodiments, in particular on different data extraction
methods, and the procedure used by the alignment unit is preferably
modified to suit any extraction method chosen by the user.
[0249] As stated above, the HF ECG signal is relatively weak
compared with the expected or typical noise level in the signal.
Consequently, the cross correlation of a template HF ECG wave with
a wave of raw data may be expected to be dominated by noise,
resulting in poor alignment results. However, the HF of the QRS
complex is considered to correlate effectively with the standard
low frequency wave, i.e. alignment of the low frequency signal will
automatically give an alignment of the HF signal. As a result, by
contrast with the method of U.S. Pat. No. 4,732,158, discussed
above, alignment is preferably carried out using the low frequency
wave. The low frequency wave generally has significantly better
S/N, and thus HF alignment may effectively be carried out on
signals with levels of noise that in fact completely mask the whole
HF ECG
[0250] Before considering the remaining sections of FIG. 5,
reference is first made to FIG. 6, which is a generalized flow
diagram describing a preferred operation of the QRS alignment unit
76 of FIG. 5.
[0251] In the process of FIG. 6, a current set of QRS complexes
(waves) is received as input in a first stage 90, preferably
together with the raw digitized signal itself and pointers for each
complex to show its location in the raw digitized signal.
[0252] In a stage 92, a first wave is selected. In a stage 94, a
reference wave is selected, preferably as has already been
described in respect of FIGS. 1 to 4. Then, in a stage 96, the
selected wave is shifted against the reference wave and a series of
cross-correlations are calculated for different relative positions
of the two waves. In a stage 98 a position corresponding to the
maximum cross-correlation is then determined as a first
approximation of the correct alignment of the selected wave. A more
accurate location is then preferably obtained in a stage 100 by
interpolation, as will be described in more detail below. In a step
102, the selected complex is aligned with the reference wave in
accordance with the interpolation result of step 100, and this is
preferably followed by a step 104 of updating of the reference
wave. If there are more waves (query step 106) then a new wave is
loaded as the selected wave (step 108) and
cross-correlation/alignment is repeated.
[0253] Particular emphasis in FIG. 6 is laid on the steps choice of
and updating of the reference wave (94 and 104) and the
interpolation of the sub-sample location of the alignment or
fiducial point (step 100).
[0254] The reference wave is preferably used to define the fiducial
point with which alignment is preferably made. Each selected wave
is aligned, using the fiducial point, with the reference wave, and
by assuming transitivity, alignment of the whole set of waves is
thus achieved. In order for transitivity to apply, the reference
wave is preferably selected and updated in such a way as to ensure
that alignment responds to changes of the wave with time (such a
change may for example be indicative of development of a Bundle
Branch Block during the exercise test) but, on the other hand does
not respond to local or transient changes (such as Premature
Ventricular Contractions, should these not have been taken care of
earlier by previous processing of the signal).
[0255] As discussed in detail above, there are many methods for the
choice of the reference wave that may be used in step 94. Among
these methods the following are preferred: [0256] 1. The first wave
in the current iteration of the process. [0257] 2. A typical wave
obtained during previous measurements or synthetically created
according to theory. [0258] 3. The method outlined above in respect
of FIG. 4, namely obtaining a set of aligned waves corresponding to
a second signal (typically which is similar to the first signal)
and executing the following steps: [0259] a. Store k (a
predetermined number) waves from that set. [0260] b. For each of
the stored waves: [0261] i. Calculate the cross-correlation of each
of the k waves with n (a predetermined number) successive waves of
the first signal. [0262] ii. For each of the k waves of the first
set determine the number, N.sub.i (0.ltoreq.i.ltoreq.k), of waves
out of the n waves of the second set with which its
cross-correlation coefficient (as calculated in step i) exceeds a
predetermined threshold. [0263] iii. From the first set of k waves
create a set of "candidates" containing all those waves with
N.sub.i (the number determined in stage ii) above a predetermined
value (depending on n). [0264] c. If the set of "candidates"
created in stage iii is empty, reiterate the process with a new set
of n waves (as in stage ii). [0265] d. If the set of candidates
created in stage iii is not empty, choose one of the candidates of
that set. The choice of the reference wave from the candidate set
may be made as follows: [0266] i. A wave for which the sum of the
calculated cross-correlation values is maximal; or [0267] ii. A
wave having a maximal number of cross-correlation values above the
predetermined threshold.
[0268] For step 104, the update of the reference wave, a few
approaches may be considered, for example: [0269] 1. Continuously
updating the reference wave. [0270] 2. Updating the reference wave
whenever a predetermined number of waves have been aligned since
the last update. [0271] 3. Updating the reference wave when the
current reference wave gives poor cross correlation coefficients
with a predetermined number of consecutive waves.
[0272] The update itself may be carried out in numerous ways,
including: [0273] 1. Repeating the procedure outlined above for the
choice of a reference wave each time an update is required. [0274]
2. Using a wave obtained by averaging over the last n waves aligned
in the process, where n is a predetermined number.
[0275] Step 100 of the process comprises interpolation of a
sub-sample location for the fiducial point. Consider, for example,
the analysis of the 150-300 Hz band of the QRS complex. An A/D
converter with a sampling rate of 1 KHz assures that no information
in the desired band is lost. However the accuracy of an A/D
converter of 1 KHz in the time domain is of 1 ms, which does not
permit sufficiently accurate alignment (at 250 Hz an error of 1 ms
corresponds to a phase shift of 90.degree.).
[0276] For each wave, the fiducial point, used as the basis for
alignment, is the point giving maximum cross correlation of the
selected wave with the reference wave (steps 96 and 98). Thus,
obtaining a sub-sample accuracy (`sub-sample accuracy` meaning
accuracy in excess of the sampling rate of the AID converter) for
the fiducial point amounts to interpolating the exact location of
the maximum of the cross correlation function CC(t) defined in step
96. This can be effectively achieved as the cross correlation
function may be considered a very smooth function. Thus relatively
incomplete data may be analytically interpolated by any method
known in the art to give an exact location of the maximum beyond
the sampling resolution. A simple method for the interpolation of
the exact location of the maximum of the cross-correlation function
involves approximating it to a polynomial of degree k (depending on
the desired accuracy) using the values of CC(t) at, say, 2 k points
about the maximum indicated by stage 98. In general the result of
interpolation stage 100 is given as a fraction corresponding to an
abstract point between two given points of the digitized signal.
Thus, in order to correctly perform the alignment the whole wave
must be shifted by that fraction (i.e. the correct value of the
signal at intermediate points (n+fraction) has to be calculated).
This can be easily achieved (e.g. by using an interpolation filter)
as the sampling rate of the signal is assumed to be adequate.
[0277] The alignment algorithm of FIG. 6 thus preferably results in
a set of aligned QRS complexes or waves. The noise reduction stage
78 of FIG. 5, may thus receive inputs as follows: [0278] 1. The set
of aligned waves. [0279] 2. The raw digitized signal. [0280] 3. For
each wave: pointers to its location in the signal.
[0281] Reference is now made to FIG. 7, which is a simplified flow
diagram showing in more detail the noise reduction stage 78 of FIG.
5.
[0282] In FIG. 7, a first step 110 indicates receipt of the above
listed inputs. In step 112 a first wave is selected for processing.
In step 114, a reference wave and a reference noise level are
selected. In a step 116, the cross-correlation function of the
aligned selected wave with the preselected reference wave
(preferably still available from the alignment stage) is compared
with a threshold. Provided that the result exceeds the threshold
the selected wave is then added in step 118 to the running set. If
the threshold is not exceeded then the selected wave is discarded
and the procedure moves on to the next wave. If the selected wave
was added then the running wave set is averaged in step 120 and
then the noise level of the newly averaged wave is compared to a
reference in a step 122. Preferably, as will be discussed in more
detail below, the step of discarding waves with low
cross-correlation to the reference wave ensures that noise is not
added to the running set prior to averaging.
[0283] Then, in decision stages 122, 124 and 126, the process ends
the noise reduction stage if the noise level has dropped below the
reference level, or if the number of waves averaged has reached a
predetermined threshold, or there are no more waves left. Otherwise
the process is repeated with the next wave in the set.
[0284] Particular emphasis in FIG. 7 is laid on the steps of the
definition of the reference wave and the reference noise level
(step 114) and the termination points of each iteration of the
algorithm (steps 122, 124, 126 and 128).
[0285] The choice of the reference wave in the procedure of FIG. 7
may be made in the same way as in the procedure of FIG. 6, and in
an implementation combining the two procedures into one; it may be
convenient to use the same reference wave.
[0286] In the procedure of FIG. 7 the reference wave is preferably
used to identify waves with comparatively high levels of noise for
exclusion from the running set. Waves whose cross-correlation
coefficient with the reference wave is not high enough to satisfy a
predetermined threshold are not incorporated into the average wave
in step 118 but are instead discarded, thus reducing the number of
waves used in the averaging process.
[0287] The reference noise level is needed to calculate the S/N
ratio in the raw signal and to assess an expected amount of
averaging needed to reach the target S/N. Alternatively, the
reference noise level may be used to determine a target S/N that is
deemed sufficient for the analysis of the signal. The target S/N is
preferably a level that may be reached by averaging over a
sufficiently small number of waves such that transient changes,
which may be of importance in the subsequent analysis, will not be
attenuated.
[0288] In either of the above cases the reference noise level is
preferably regarded as an input for a function, preferably supplied
by the user, that defines when the S/N level of the averaged signal
has reached a satisfactory level (step 122) and that also defines a
threshold on the maximal size of the set of averaged waves (step
124).
[0289] Whichever of the two possibilities is selected for use of
the reference noise level as described above, the reference noise
level is preferably calculated from a signal to noise analysis of
the raw signal in the following way: [0290] a. Using the pointers
of the waves in the current set to indicate correct points of
origin of the respective waves in the initial raw signal, and with
access to that signal, segments are preferably selected over which
the reference noise level is to be computed, as segments in the
"neighborhood" of the waves in the set. Alternatively, the noise
level may be calculated using any segment of the raw signal. [0291]
b. Define a set of "quiet" segments: [0292] i. If the set of waves
does not cover the whole neighborhood of the segment to be
considered, (that is, in the given segment there are intervals
between some of the waves) preferably choose new segments out of
these intervals. [0293] ii. Alternatively, choose a number of waves
from the neighborhood of a segment to be considered. In each wave
find an interval during which there is a low signal level, and
choose segments for noise analysis from these intervals. [0294]
Regardless of the way the "quiet" segments have been chosen, it is
preferable to ensure that the total length of the segments selected
for noise analysis surpasses a predetermined threshold. [0295] c.
Defining a reference noise level based upon the noise levels of the
segments selected in the previous stages. The noise level in each
segment can be defined, for example as the root mean square (RMS)
of the signal in that segment.
[0296] Each wave not eliminated after step 116, is now preferably
incorporated into the running set, that is to say along with all
previous waves of the same set which have not been eliminated. At
each iteration, the average wave of the updated set is preferably
calculated (step 120).
[0297] The noise level of the average wave is now calculated in
step 122 as described above. Step 122 bears close attention as it
ensures that as soon as the desired S/N ratio of the average wave
(as determined by the user) has been reached no further averaging
of the present set takes place, thereby reducing attenuating
effects due to surplus noise reduction. As long as the required S/N
has not been attained the algorithm keeps adding new waves to the
averaged set, until a threshold number of waves (preferably
determined according to a function supplied by the user) are
reached. When such a threshold is reached (step 124) iteration on
the current waveset is stopped, and a new iteration process starts
with the next waveset (segment etc.) in the queue, as discussed
with regard to the continuation of the process of FIG. 1 after step
20.
[0298] Returning now to FIG. 5, and after the termination of the
noise reduction procedure (section 78), preferably the resulting
S/N ratio of the averaged wave, in the frequency band of interest,
is favorable. Now, in a filter unit 80, the averaged waves are
preferably band-pass filtered to leave only the frequency band of
interest. The design of the band-pass filter for the processed,
noised reduced standard ECG QRS complexes obtained from the
previous stages preferably takes into account several parameters
including: [0299] 1. The locality of the phenomenon in the time
domain (that is to say, when looking for a given phenomenon in the
QRS complex--either in the standard ECG or the HF ECG--what is the
coarsest time resolution in which we may reasonably expect to be
able to detect the phenomenon?). [0300] 2. The locality of the
phenomenon in the frequency domain. It is noted that important
phenomena in the HF ECG do not have any trace in the standard ECG.
As the energy levels of the ECG signal in the lower band of the
spectrum are much higher, the locality of a phenomenon in the
frequency domain is defined as the largest frequency range in which
the low frequency signal will not conceal it.
[0301] Filter parameters selected in accordance with either one of
the two considerations above may be mutually exclusive. In practice
an optimal filter is selected based on the individual traits of the
phenomenon to be dealt with.
[0302] It should be stressed that different implementations of the
algorithm may call for different filters and different frequency
bands. These parameters should preferably be selected in accordance
with the requirements of the data analysis in the given case being
taken into account.
[0303] Although, for the purpose of the alignment procedure it has
been assumed above that the HF signal of the QRS complex is highly
correlated with the low frequency or standard QRS, local phenomena
in the low frequency ECG, which have no trace in the HF ECG (e.g.
transient "notches" that appear or disappear during exercise) may
cause slight deviations in the alignment of the HF signal.
Realignment of the filtered waves (say as in step 102 in FIG. 6,
with the band-pass filtered signals as input) will usually
eliminate such deviations.
[0304] Preferably, an additional stage of noise reduction (not
appearing in the diagram of FIG. 5) may be applied to the set of
filtered and realigned waves. More precisely, after the first noise
reduction procedure (unit 78) has reduced the S/N ratio in the band
of interest to a level that makes it possible to align the
band-pass filtered waves, a reiteration of the procedure of FIG. 7
may be carried out using the aligned band-pass filtered set of
waves as input. Partitioning the noise reduction process into two
stages has two main advantages: [0305] 1. Reiterating the alignment
algorithm with the band-pass filtered waves helps exclude certain
extremely noisy or abnormal waves wherein the anomaly is not
detectable in the standard, or low frequency ECG signal, (it will
be recalled that initial alignment was carried out using the low
frequency version of the wave) and [0306] 2. As stated above,
alignment using band-pass filtered waves gives a better alignment
of the HF ECG than that achieved using standard ECG, thus reducing
the risk of attenuation, during the averaging procedure, of
important data contained in the signal.
[0307] It is noted that, in order to implement the system of FIG. 5
using the partitioning of the noise reduction procedure as outlined
above, the user may preferably supply the procedure of FIG. 7 with
two different noise level targets (as in stage 7 of that algorithm)
as follows: [0308] 1. A noise level that allows an efficient
alignment of the band-pass filtered waves. (The "target noise" for
the first iteration), and [0309] 2. The desired final noise level,
required for the inspection and analysis of the signal. (The
"target noise" for the second iteration).
[0310] In an alternative embodiment, the fine alignment procedure
(using fine alignment unit 81 in FIG. 5) may be dispensed with. In
its place, a procedure utilizing the following steps is preferably
adopted: [0311] 1. Proceed with signal acquisition, signal
amplification and digitization, QRS complex detection and QRS
complex alignment, thereby to create an array of aligned waves
complete with pointers to their correct original location in the
raw signal. [0312] 2. Use the aligned waves to create an array of
waves with relatively low level of noise in the frequency band of
interest. This can be done as described above in respect of the
noise reduction unit 78 of the system of FIG. 5, or more simply by
averaging a predetermined number of consecutive waves in the array.
[0313] 3. Band-pass filters each of the waves obtained at the
previous stage to the desired frequency range to create a set of
reference HF waves. [0314] 4. Band-pass filters each of the waves
obtained by signal acquisition unit 70 to the desired frequency
range to form an array of HF waves. [0315] 5. Proceed with the
alignment and noise reduction procedures of units 76 and 78 in the
system of FIG. 5 (as described in FIGS. 6 and 7 respectively) using
the set of HF waves created in the previous unit as input and with
the array of averaged and band-passed waves from preceding stages
or otherwise obtained as reference waves.
[0316] The above procedure avoids relying on the low frequency
signal for performing alignment, thus bypassing the inaccuracies
referred to above requiring a separate fine alignment
procedure.
[0317] Preferably, each segment selected for examination contains a
QRS complex and only one such complex. Thus, the restriction of the
alignment procedure to a single wave at a time makes it possible to
perform alignment over waves with relatively high S/N ratio.
Furthermore, the cross-correlation function of the LF signals (See
FIG. 6 above) preferably gives a clear indication as to where in
each segment to look for the fiducial point corresponding to that
segment.
[0318] However, as the S/N ratio in the frequency band of interest
is typically much lower than in the standard low frequency part of
the ECG signal there may be circumstances in which the S/N in the
required frequency band will not permit alignment in that band even
using the above-described procedure, whilst in the standard ECG, an
acceptable alignment could yet be achieved. Thus in order to
implement the above-described modified procedure some assumption
regarding the S/N in the band of interest is preferably made to
give a yardstick to determine whether or not the alignment
according to the modified procedure is sufficiently accurate.
Generally, in the case of the QRS complex, the part thereof being
of interest and having the higher noise level is a high frequency
component thereof.
[0319] As mentioned above, analysis of the HF QRS preferably serves
as a diagnostic tool for early detection of ischemia. Therefore,
various data extraction procedures (unit 84 of FIG. 5) described in
the following section focus on the extraction of those parameters
currently believed to be diagnostically significant. The skilled
person will appreciate that future developments may add new
parameters to the list of those believed to be diagnostically
significant and may render some or all of those currently so
regarded as redundant or otherwise insignificant. The skilled
person will thus appreciate the need to modify the data extraction
unit 84 in the light of such developments.
[0320] In the following, for the sake of simplicity it will be
assumed that data extraction procedures described below have access
to the following data: [0321] 1. An array of aligned noise reduced
HF QRS waves. [0322] 2. An array of aligned standard ECG waves.
[0323] 3. For each wave, pointers to its correct location in the
raw signal. [0324] 4. The raw signal. [0325] 5. The band-pass
filtered raw signal. [0326] 6. Any other parameter calculated at
each of the previous stages even if it was not specifically
mentioned that it has been stored in any kind of storage device,
including, but not limited to, the cross-correlation of each wave
with the relevant reference wave, and the S/N ratio in the signal
at different stages of the test being carried out on the
patient.
[0327] The following specific embodiments of the data extraction
unit 84 are taken from experiments which explored the 150-250 Hz
frequency band. The embodiments are not, of course, in any way
limited to those specific frequencies.
[0328] As shown by Beker et al, a decrease in the total energy of
the HF QRS signal during exercise gives a strong correlation with
an ischemic condition of the heart. In what follows the RMS of an
HF QRS signal is taken to represent the energy. A decrease in the
total energy of the signal during exercise can be looked for in any
of the following ways: [0329] 1. Choose an arbitrary point during a
rest period and compute the RMS of the HF QRS at that point.
Compare that value with the RMS value of an HF QRS at an arbitrary
point at peak exercise. [0330] 2. Proceed as in 1 above except that
the reference point at rest is chosen as the one with minimum RMS
value. [0331] 3. Proceed as in 1 or 2 above where the reference
point during exercise is chosen right after peak exercise. [0332]
4. Compute the mean RMS value of the HF QRS over the entirety of a
rest stage of the test and compare it to the RMS value at a point
of time during an exercise stage. [0333] 5. Proceed as in 4 above
where a mean RMS calculated for a rest stage is compared with a
mean RMS calculated for the exercise stage. [0334] 6. Proceed as in
5 where the mean RMS over the exercise stage is taken over a period
of relatively stable heart rate. [0335] 7. Proceed as in any of 1-6
with any of a set of leads for obtaining ECG signals or any
combination of the leads. For example: [0336] a. The group of leads
may include all the precordial leads, all the frontal leads or any
other--less standard partition of the leads, for example according
to those areas of the heart that they cover. [0337] b. Proceed as
in 1-6 for non-conventional ECG leads corresponding to areas of the
heart that are not significant enough in the standard ECG leads.
[0338] c. Proceed as in a or b where the analyzed signal is the
vectorial sum of the signals in the group (i.e. given a group of
leads, combining it into a single vector by summing all the leads
in that group, taking into account their spatial position). [0339]
8. Proceed as in any of 1-7 where the RMS is not calculated over
the whole QRS complex but over any predetermined portion of the
wave. The size and location of the portion of the wave to be
examined may be given either in absolute values relative to a
fiducial point in the wave (e.g. a portion of 30 ms starting 20 ms
after the onset of the signal) or relative to the size of each wave
(e.g. that 50% of the wave starting at the beginning of its second
quarter).
[0340] In a paper by Abboud (Progress in Cardiovascular Diseases
Vol XXXV. No. 5 March/April 1993) it has been demonstrated that
transient ischemia in patients undergoing percutaneous transluminal
coronary angioplasty (PTCA) of a critical stenosis in the left
anterior descending (LAD) coronary artery can be detected in the HF
QRS wave. It has been shown that the inflation of the balloon (and
the transient ischemia it induces) corresponds to a sensible
decrease on a graph depicting the normalized cross-correlation
coefficient of a (constant) template HF QRS and the real signal.
Thus, any of the methods of sections 1-8 of the previous paragraph
can be applied using the cross-correlation function in place of the
RMS function or in addition thereto.
[0341] Thus, analysis of the HF ECG may serve as a very sensitive
non-invasive diagnosis tool for the detection of ischemia.
Furthermore, the methods for HF ECG analysis suggested herein may
be designed to supply on-line results without using hardware
additional to standard ECG equipment. Thus, the present embodiments
may be readily applied in all situation where standard ECG is used
to monitor the heart's condition. Thus, different specific
embodiments of the said methods include, among others:
[0342] 1) Screening exercise tests for early detection of ischemia.
The methods suggested herein may serve to gauge the patient's HF
ECG signal at rest (i.e. before exercise), the evolution of the
signal under stress (i.e between rest and peak exercise) and during
a recovery period (i.e. from peak exercise till the heart rate
returns to its normal level) carry out a comparison and thus
extract diagnostically significant data. The information thus
obtained can be compared to the data of previous exercise tests
undergone by the same patient, in order to follow the evolution of
the cardiac condition over time.
[0343] 2) Monitoring the evolution over time of coronary perfusion
in CAD patients undergoing a drug treatment or during a
rehabilitation period after cardiac surgical interventions. That
is, using data of several consecutive HF ECG tests over a
relatively short period of time (several days or weeks) it is
possible to monitor the improvement in the coronary perfusion of
the patient in order to assess the effectiveness of the
treatment.
[0344] 3) ER and surgery room on-line monitoring of patients during
heart failure such as acute myocardial infarction or during PTCA.
On-line analysis of the HF ECG in such situations may help the
diagnosis in ERs (whether or not the patient suffers from ischemia)
and to assess the immediate improvement of the coronary perfusion
during a cardiac surgical intervention.
[0345] 4) Integration of the embodiments into any (not necessarily
cardiac) monitoring systems, including but not in any way limited
to standard ECG monitoring.
[0346] In addition, the various embodiments of the present
invention may be applied to the extraction of a low amplitude
signal from input containing a high amplitude signal masking the
low amplitude signal. That is, as long as noise is not correlated
with the low amplitude signal that it masks the embodiments of the
present invention are applicable, even if the noise is not evenly
distributed in the spectrum domain. A typical embodiment of the
method in such a case is fetal ECG monitoring, where the signal of
interest is masked by the mother's much stronger signal. In that
case the method is applied to detect the mother's ECG, and to
create therefrom a set of dynamically chaining templates that may
subsequently be subtracted from the original data, thus to leave
only the fetal ECG.
[0347] In accordance with the above described embodiments there are
thus provided embodiments of the present invention which provide in
various aspects:
[0348] 1) The use of the high frequency part of an ECG signal, more
specifically the high frequency components of the QRS complex, in
early detection of cardio-vascular infarction, tests involving the
high frequency part of the signal being typically more sensitive
than the standard ECG signal. In general, a decrease of the HF ECG
of the QRS complex during stress test serves as an indication of
such a condition.
[0349] 2) Alignment of high frequency parts of ECG waves using the
low frequency parts of the waves.
[0350] 3) Reducing eccessive-noise related artifactes by by
selecting waves having highest cross-correlation levels with a
reference wave.
[0351] 4) A noise reduction procedure involving successive
averaging of aligned waves, the procedure being modified for
minimal distortion of an overall waveform by selecting a
predetermined SNR and terminating iteration when the predetermined
SNR is reached.
[0352] 5) Extraction of an RMS of a high frequency component of a
QRS complex of an ECG signal to determine whether there is a
successive decrease in the level of such an RMS. The presence of
such a successive decrease may be used to indicate the probability
of the presence of ischemia.
[0353] 6) Extraction of a cross-correlation function of a high
frequency component of a QRS complex of an ECG signal to determine
whether there is a successive decrease in the level of such a
function. The presence of such a successive decrease may be used to
indicate the probability of the presence of ischemia.
[0354] Generally, the probability of the presence of any of the
conditions referred hereinbefore in the human (or animal) body
infers a signal given by a machine to draw the attention of medical
personnel to the possibility of the presence of a condition.
[0355] It is appreciated that certain features of the invention,
which are, for clarity, described in the context of separate
embodiments, may also be provided in combination in a single
embodiment. Conversely, various features of the invention which
are, for brevity, described in the context of a single embodiment,
may also be provided separately or in any suitable
subcombination.
[0356] It will be appreciated by persons skilled in the art that
the present invention is not limited to what has been particularly
shown and described hereinabove. Rather the scope of the present
invention is defined by the appended claims and includes both
combinations and subcombinations of the various features described
hereinabove as well as variations and modifications thereof which
would occur to persons skilled in the art upon reading the
foregoing description.
* * * * *