Title: Signal evaluation method for detecting QRS complexes in electrocardiogram signals
Abstract: A signal evaluation method for detecting QRS complexes in electrocardiogram (ECG) signals comprises the following steps:
- sampling of the ECG signal (4) and conversion into discrete signal values (x(n)) in chronological order;
- comparing the signal values (xf(n), xfq(n)) to a threshold function (K(n)) adaptively determined therefrom;
- determining a frequency number (D(n)) within a defined segment of the consecutive signal values, by which signal values (xf(n), xfq(n)) preferably fall short of the threshold function (K(n));
- comparing the determined frequency number (D(n)) to a defined threshold (Θ), wherein an undershoot of the threshold (Θ) is significant for apresence of a QRS complex (5, 6, 7) in the defined segment of the ECG signal (4).
Patent Number: 6,937,888 Issued on 08/30/2005 to Köhler, et al.
| Inventors:
|
Köhler; Bert-Uwe (Berlin, DE);
Orglmeister; Reinhold (Berlin, DE)
|
| Assignee:
|
Biotronik GmbH & Co. KG (Berlin, DE)
|
| Appl. No.:
|
067391 |
| Filed:
|
February 7, 2002 |
Foreign Application Priority Data
| Feb 07, 2001[DE] | 101 05 431 |
| Current U.S. Class: |
600/521 |
| Intern'l Class: |
A61B 005/04 |
| Field of Search: |
600/509,508,521
|
References Cited [Referenced By]
U.S. Patent Documents
| 3552386 | Jan., 1971 | Horth.
| |
| 5701907 | Dec., 1997 | Klammer.
| |
| 5836982 | Nov., 1998 | Mühlenberg et al.
| |
| Foreign Patent Documents |
| 196 26 353 | Jan., 1998 | DE.
| |
Other References
Jiapu Pan et al. "A Real-Time QRS Detection Algorithm", IEEE Transactions on
Biomedical Engineering, vol. BME-32, No. 3, Mar. 1985.
G. M. Friesen et al. "A Comparison of the Noise Sensitivity of Nine QRS Detection
Alogrithms", IEEE Transactions on Biomedical Engineering, IEEE Inc. New York, US,
Bd. 37, Nr. 1, 1990, Seiten 85-98.
|
Primary Examiner: Bockelman; Mark
Attorney, Agent or Firm: Browdy and Neimark, P.L.L.C.
Claims
1. A signal evaluation method for detecting QRS complexes in an electrocardiogram
(ECG) signal, comprising the following steps:
sampling the ECG signal to produce consecutive sampled signal values;
converting the ECG sampled signal values into consecutive discrete signal values
of chronological order;
comparing the discrete signal values to a threshold function adaptively determined
from the discrete signal values;
determining a frequency number within a defined segment of the consecutive discrete
signal values, the frequency number being representative of the number of discrete
signal values that are below the threshold function; and
comparing the determined frequency number to a defined frequency number threshold,
wherein the presence of a QRS complex in the defined segment of the ECG signal
is indicated when the determined frequency number is less than the frequency number
threshold.
2. The signal evaluation method according to claim 1, wherein said step of converting
comprises subjecting the sampled ECG signal values to a high-pass filtering.
3. The signal evaluation method according to claim 1, wherein said step of converting
comprises subjecting the sampled EGG signal values to a band-pass filtering.
4. The signal evaluation method according to claim 3, wherein upper and lower
limiting pass frequencies of the band-pass filter are approximately 18 Hz and approximately
27 Hz.
5. The signal evaluation method according to claim 3, wherein said step of converting
further comprises generating absolute values of the filtered signal values.
6. The signal evaluation method according to claim 5, wherein said step of generating
absolute values is carried out by mathematically squaring the filtered signal values.
7. The signal evaluation method according to claim 6, wherein the value of the
threshold function is determined adaptively from a flowing averaging of the squared
signal values for an averaging period determined by a memory factor.
8. The signal evaluation method according to claim 1, wherein the value of the
threshold function is determined adaptively from a flowing averaging of the discrete
signal values, for an averaging period determined by a memory factor.
9. The signal evaluation method according to claim 1, wherein the frequency number
threshold is variably set as an adaptive threshold from the frequency number itself.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention relates to a signal evaluation method for detecting QRS complexes
in electrocardiogram (ECG and IEGM) signals.
2. Background Art
Regarding the background of the invention, it can be stated that the automatic
analysis of ECG signals is playing an increasingly larger role in perfecting the
functionality of cardiac pacemakers and defibrillators. Newer models of implantable
cardiac devices of this type accordingly also offer the capability to perform an
ECG analysis. The detection of QRS complexes and R spikes in ECG signals plays
an extremely important role in this context. This significance results from the
many and diverse applications for the information concerning the time of occurrence
of the QRS complex, for example when examining the heart rate variability, in the
classification and data compression, and as the base signal for secondary applications.
QRS complexes and R spikes that are not detected at all or detected incorrectly
pose problems with respect to the efficiency of the processing and analysis phases
following the detection.
A wide overview of known signal evaluation methods for detecting QRS complexes
in ECG signals can be found in the technical essay by Friesen et al. "A Comparison
of the Noise Sensitivity on Nine QRS Detection Algorithms" in IEEE Transaction
on Biomedical Engineering, Vol. 37, No. 1, January 1990, pages 85-98. The signal
evaluation algorithms presented there are based throughout on an evaluation of
the amplitude, the first derivation of the signal, as well as its second derivation.
For the presented algorithms, the essay distinguishes between those that perform
an analysis of the amplitude and the first derivation, those that analyze only
the first derivation, and those that take into consideration the first and second
derivation. To summarize briefly, all algorithms check whether the given signal
parameter exceeds or falls short of any predetermined thresholds, after which,
if such an event occurs, the occurrence of additional defined events is checked
based on a predefined pattern, and if certain criteria are fulfilled, the conclusion
is drawn that a QRS complex is present.
Another aspect in the signal evaluation for detecting QRS complexes needs
to be taken into account when methods of this type are implemented in implanted
cardiac devices. In view of the natural limitations of these devices regarding
their energy supply and computing capacity, it is important that the detection
of QRS complexes can be performed with the simplest possible algorithms with the
fewest possible mathematical operations on the basis of whole numbers instead of
real numbers.
Signal processing methods from the fields of linear and non-linear filtering,
wavelet transformation, artificial neural networks and genetic algorithms have
also been applied in the QRS detection. With large signal-noise distances and non-pathological
signals, i.e., when good signal conditions are present, these evaluation methods
produce reliable results. When no such conditions were present, the efficiency
of the evaluation processes could drop drastically, which, of course, is not acceptable
with regard to the reliable operation of a pacemaker.
Finally, QRS detection on the basis of zero crossing counts is known from
Applicant's prior German patent application no. 100 11 733.3 which has however
been published subsequently. It comprises the following process steps:
- sampling of the signal and conversion into discrete signal values in
chronological order;
- high-pass filtering of the sampled signal values;
- determining the sign of each signal value;
- continuous checking of the signs of consecutive signal values for the
presence of a zero crossing between two consecutive signal values;
- determining the number of zero crossings in a defined segment of the
consecutive signal values; and
- comparing the determined number of zero crossings to a defined threshold
value, with a lower deviation from the threshold value signifying the presence
of a QRS complex in the defined segment of the signal curve.
So as to in this case obtain a significantly high number of zero crossings in
the range outside the QRS complexes, a high-frequency overlay signal of low amplitude
as compared to the amplitude of the QRS complex is added to the high- or band-pass
filtered and squared ECG signal.
Of course, this way of proceeding conflicts with the demand, explained at the
outset, for simplest possible algorithms.
SUMMARY OF THE INVENTION
Based on the described problems, the invention has as its object to present
a signal evaluation method for detecting QRS complexes in ECG signals that can
be used with a comparatively low computing capacity and also with problematic signal
conditions while producing reliable detection results.
This object is met with the process steps according to the invention as follows:
- sampling of the ECG signal and conversion into discrete signal values
of chronological order;
- comparing the signal values to a threshold function adaptively determined therefrom;
- determining a frequency number within a defined segment of the consecutive
signal values, by which preferably the absolute values of the signal values fall
short of the threshold function;
- comparing the determined frequency number to a defined threshold, wherein
an undershoot of the threshold is significant for the presence of a QRS complex
in the defined segment of the ECG signal.
The core element of the inventive method is the application of a threshold comparison
and a subsequent frequency count, based on utilizing the morphology of the QRS
complex. The QRS complex in the ECG signal is characterized by a relatively high-amplitude
oscillation that markedly guides the signal curve away from the regularly noisy
and offset-actuated zero line of the electrocardiogram. The frequency of this short
oscillation lies within a range in which other signal components, such as the P
and T wave, exert only minor influence and can be removed preferably by pre-filtering-for
example high-pass or band-pass filtering. After suppression of these low-frequency
signal components, signal fluctuations result around the zero line, due to higher-frequency
noise, that dominate the ECG signal in the range where no QRS complex occurs. The
QRS complex then appears in this signal context as a slow, high-amplitude oscillation
of only short duration that significantly leads away from the zero line of the
ECG signal. If the signal values are compared to a threshold function representative
of the signal noise, the amounts of the signal values outside the QRS complex mostly
undershoot this threshold function. In this regard, the frequency number is great
by which the amounts of the signal values undershoot the threshold function. In
the range of the QRS complex, the amounts of the signal values significantly exceed
the threshold function. Consequently, a small frequency number of undershoots of
the threshold is found in the course of the QRS complex. So the QRS complex can
be selected by comparison of the determined frequency number with a defined threshold
value. An undershoot of the threshold value signifies the overshoot of the threshold
function that is typical of the QRS complex.
The method, according to the invention, of QRS complex detection has proven robust
with regard to noise interference and easy to implement with respect to the computing
technology. In this regard, it is particularly suitable for implementation in the
real time analysis of ECG signal morphologies in cardiac pacemakers.
The previously mentioned high-pass filtering is performed preferably with a low
pass frequency of 18 Hz. In this way, the low-frequency components, such as the
P and T waves as well as a base line drift, can be suppressed. Furthermore, the
QRS complex thus becomes the signal component with the lowest frequency that dominates
the signal during its occurrence.
To increase the sign-noise distance, provision may furthermore be made to square
the signal values prior to comparing them to the threshold function and the frequency
number. As a result, smaller signal values are weakened relative to larger signal
values, which further improves the detectability of the QRS complex.
The value of the threshold function is preferably determined adaptively from
a flowing determination of the average of the band-pass filtered and squared signal values.
Details of the method according to the invention will become apparent from
the ensuing description of a exemplary embodiment, taken in conjunction with the drawing.
BRIEF DESCRIPTION OF THE DRAWING
FIG. 1 is a highly schematic illustration of the signal curve of a QRS complex
in an ECG signal; and
FIG. 2 is a structural diagram of the signal evaluation method according to
the invention for detecting QRS complexes in ECG signals.
DESCRIPTION OF THE PREFERRED EMBODIMENT
As seen in FIG. 1, an idealized QRS complex consists of a relatively high-amplitude
oscillation about the zero line
1 that initially guides the ECG signal
4,
in the Q spike
6, away from the zero line
1 in a negative direction.
Afterwards the ECG signal
4 is guided, in the R spike
5, into the
positive range with a steep rise and with a subsequent steep drop back into the
negative range while forming the S spike
7.
In reality, the ECG signal
4 is accompanied by a certain level of noisiness,
as indicated in FIG. 1 by the dashed signal curve. If this noisy signal is sampled
and converted into discrete signal values of chorological order and band-pass filtered,
these signal values can be compared to the threshold function K(n) that is diagrammatically
illustrated in FIG. 1 as a crisscrossed line. As can be derived clearly and by
way of model from FIG. 1, the value of the ECG signal in the range outside the
QRS complex mostly falls short of this threshold function K(n). In the range N
1
for instance, significantly high frequencies result for signal values |x(n)| below
the threshold function K(n).
In the range of the Q spike
6, the value of the ECG signal deviates very
strongly from the threshold function K(n) in the positive direction. The frequency
number D(n) within the segment N
2 of the QRS complex
6 for this event
is considerably smaller than the frequency number D(n) within the segment N
1.
In this regard, the frequency number D(n) may be utilized for detecting the QRS
complex, the presence of which is detected when the frequency number D(n) undershoots
a defined threshold Θ.
Emphasis must be laid on the fact that a gist of the invention as compared
to the prior art resides in that, based on the detection of the mentioned frequency
number found and the comparison thereof to a defined threshold, the amplitude of
the ECG signal is not checked as to whether a certain threshold is absolutely exceeded
for conclusion therefrom on the QRS complex; this is the prior art way of proceeding.
Rather, sort of a check is carried out as to how long the ECG signal clearly remains
on a side of the threshold function that speaks in favor of the presence of a QRS
complex. Only the presence of a certain duration of this condition is used as a
conclusion that points to the presence of a QRS complex. Consequently, strong measuring
fluctuations of only short duration are not detected as (false) QRS complexes (so-called
false positive errors).
The detailed sequence of the inventive evaluation method will be explained in
detail, based on FIG.
2.
The ECG signal
4 is sampled and converted into discrete signal values
x(n) of chronological order. The sampling rate may be f
t=360 Hz, for
example, i.e., the ECG signal is converted into a sequence of 360 measuring values
per second. The sampled ECG signal x(n) is then subjected, on the input side, to
a band-pass filtering BP that serves to remove all the signal components that do
not belong to the QRS complex. This includes P and T waves as well as high-frequency
noise that may originate, for example, from the bioelectrical muscle activity.
The applied filter BP is linear-phase, non-recursive and has a band-pass characteristic
with the pass frequencies f
g1=18 Hz and f
g2=27 Hz as well
as the limiting cutoff frequencies f
s1=2 Hz and f
s2=50 Hz.
The filter order is FO=26. The group delay of the band-pass filter BP accordingly
corresponds to 13 sampling values and must be taken into consideration when determining
the time of the occurrence of the QRS complex.
The signal values x
f(n) attained in this manner are subsequently squared
in a squaring step QS according to the following relation:
|x
f(n)|
2
The values x
fq(n) thus prepared from the original signal values x(n)
by a kind of computation of an absolute value are fed to a comparator complex HZ
that compares these signal values to a threshold function K(n) adaptively determined
therefrom. The process complex that is concerned with the determination of the
threshold function K(n) is designated by AS in FIG.
2. In this complex,
an appropriate value for the function coefficients K(n) is adaptively estimated
from the signal values x
fq(n). To this end, the band-pass filtered and
squared signal values are recursively determined by flowing averaging by the aid
of a memory factor λ
k(0<λ
k<1),
with c being a constant.
Empirically, λ
k=0,98 and c=8 result as appropriate values.
The averaging time given by the memory factor λ
k substantially
determines the adaptation rate of this estimate, with too short as well as too
long averaging signals affecting the efficiency of the signal evaluation method.
In the process complex HZ, the signal values x
fq(n) are compared to
the threshold function K(n)-as mentioned. In doing so, the direction is found in
which the signal values x
fq(n) deviate from the threshold function K(n).
A frequency number D(n) within this defined segment N is determined therefrom,
representing the number or frequency of events for which the signal values x
fq(n)
fall short of the threshold function. In a favorable way of calculating, D(n) may
also be determined recursively via
##EQU1##
A smaller amount of the frequency number D(n) indicates that the amount of the
ECG signal
4 durably exceeds the threshold function K(n), which is a reliable
parameter for the presence of the QRS complex.
In the course of the method according to the invention, a threshold Θ still
has to be determined, the undershoot of which significantly indicates the presence
of a QRS complex in the defined segment of the ECG signal
4. Θ(n)
is recursively computed from D(n) by
with a memory factor 0<λ
θ<1 being used. This memory factor
for example can be selected to be λ
θ=0,99. If D(n) falls
short of the threshold Θ, a QRS complex has been detected, otherwise it has not.
The job of checking whether the above requirement has been fulfilled takes place
in the decision stage according to FIG.
2.
The evaluation method according to the invention can be realized by implementation
based on a software-based solution in the form of a corresponding evaluation program
but also by a realization based on a hardware-based solution by means of a corresponding
electronic evaluation assembly.
*
