CN113197584A - QRS wave group identification method based on difference zero-crossing detection method - Google Patents
QRS wave group identification method based on difference zero-crossing detection method Download PDFInfo
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Abstract
The invention discloses a QRS complex identification method based on a differential zero-crossing detection method, which comprises the following steps: filtering the acquired original electrocardiosignals by using a self-adaptive baseline drift filter, and removing direct current bias of the original electrocardiosignals to obtain electrocardiosignals fluctuating up and down near a value of 0; filtering the electrocardiosignal by using a 15Hz Butterworth low-pass filter, filtering power frequency interference and inhibiting myoelectric interference; carrying out first-order and second-order difference on the electrocardiosignals after filtering processing, analyzing the probability distribution of numerical values after the first-order difference, and obtaining a threshold value for detecting an R peak; and sequentially finding an S valley and a Q valley through second-order difference, calculating the heart rate and the wavelength of Q, R, S waves according to the sampling frequency, extracting waveform characteristics and finishing the identification of the QRS wave group. The invention can improve the working efficiency, has small calculated amount, high real-time performance and extremely low calculation force requirement on the main control chip, and can be widely applied to household portable ECG monitors and telemedicine with lower price.
Description
Technical Field
The invention relates to the technical field, in particular to a QRS complex identification method based on a differential zero-crossing detection method.
Background
Because the electrocardiograph must record the electrocardiographic data of a patient for diagnosis for a long time due to the instantaneity and paroxysmal nature of some cardiovascular diseases, the acquired data amount is huge, and the diagnosis and analysis by naked eyes are difficult. The electrocardiogram is divided into P wave, PR interval, QRS wave, ST segment and T wave in medicine, wherein the QRS wave represents the condition of ventricular depolarization, and diseases such as ventricular premature beat, atrial fibrillation, atrial flutter, coronary heart disease and the like can be detected by detecting the QRS wave, so that the method has certain significance for the automatic detection of the QRS wave.
At present, methods such as wavelet transformation, neural network, template matching, Teager energy operator and the like are provided for QRS waveform detection, the wavelet transformation has the advantage of low false detection rate, but real-time detection is difficult to achieve in a portable electrocardiogram monitor with low performance due to huge calculation amount, the neural network method is difficult to widely apply due to the fact that training period is long, and electrocardio waveforms with large differences with a training set are difficult to distinguish, template matching is easy to achieve, but the problem that waveforms with low correlations with the templates are difficult to distinguish is also existed, the Teager energy operator distinguishes by tracking instantaneous energy changes of tracking signals, R peaks can be effectively detected, and the distinction of Q points and S points is slightly struggled.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: the large calculation amount in the prior art is difficult to perform real-time detection in a portable electrocardiogram monitor with weak performance, and the method based on the neural network is difficult to be widely applied due to the fact that the training period is long and the electrocardiogram waveform with large difference with the training set is difficult to distinguish.
In order to solve the technical problems, the invention provides the following technical scheme: filtering the acquired original electrocardiosignals by using a self-adaptive baseline drift filter, and removing direct current bias of the original electrocardiosignals to obtain electrocardiosignals fluctuating up and down near a value of 0; filtering the electrocardiosignal by using a 15Hz Butterworth low-pass filter, filtering power frequency interference and inhibiting myoelectric interference; carrying out first-order and second-order difference on the electrocardiosignals after filtering processing, analyzing the probability distribution of numerical values after the first-order difference, and obtaining a threshold value for detecting an R peak; and sequentially finding an S valley and a Q valley through second-order difference, calculating the heart rate and the wavelength of Q, R, S waves according to the sampling frequency, extracting waveform characteristics and finishing the identification of the QRS wave group.
As a preferred embodiment of the QRS complex identification method based on the differential zero-crossing detection method of the present invention, wherein: the time domain of the adaptive baseline shift filter includes,
y[n]＝x[n]-B[n]
where x [ n ] represents the filter input value, y [ n ] represents the filter output value, and B [ n ] represents the baseline wander of the fix-up.
As a preferred embodiment of the QRS complex identification method based on the differential zero-crossing detection method of the present invention, wherein: the baseline wander amount of the repair includes,
where W represents the window width.
As a preferred embodiment of the QRS complex identification method based on the differential zero-crossing detection method of the present invention, wherein: segmenting QRS complexes and other complexes using the first order difference includes,
y[n]＝x[n]-x[n-1](n≥1)
as a preferred embodiment of the QRS complex identification method based on the differential zero-crossing detection method of the present invention, wherein: amplifying the first order difference result, i.e. the difference value, using a power function as a mapping function to distinguish the QRS complex from other complexes, wherein the mapping function comprises,
y[n]＝(x[n])3
as a preferred embodiment of the QRS complex identification method based on the differential zero-crossing detection method of the present invention, wherein: the R peak and the S valley comprise that the zero crossing of the differential value between the time intervals of the wave peak and the wave valley is the time when the R peak appears in the electrocardiosignal, and the next zero crossing of the differential value is the time when the S valley appears in the electrocardiosignal.
As a preferred embodiment of the QRS complex identification method based on the differential zero-crossing detection method of the present invention, wherein: the Q, R, S three points are obtained by utilizing a zero-crossing detection algorithm, a proper threshold value and a window size are set, if the difference value is changed from positive to negative, whether the difference value is the R point is judged according to the threshold value, if the difference value is changed from negative to positive, the difference value is judged to be the S point, and the point which is changed from negative to positive before the R peak appears is judged to be the Q point.
As a preferred embodiment of the QRS complex identification method based on the differential zero-crossing detection method of the present invention, wherein: the adaptive baseline shift filter further includes defining the window width to be one tenth of a sampling frequency value.
The invention has the beneficial effects that: the invention can detect the data collected by the electrocardiograph in real time and detect the QRS wave and measure the wavelength, thereby greatly shortening the time for a doctor to find an abnormal waveform in a large number of electrocardiographic waveforms and improving the working efficiency; the invention has small operand, high real-time performance and extremely low computational power requirement on the main control chip, thereby being widely applied to household portable ECG monitors and telemedicine with lower price.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic basic flowchart of a QRS complex identification method based on a differential zero-crossing detection method according to an embodiment of the present invention;
fig. 2 is another basic flowchart of a QRS complex identification method based on a differential zero-crossing detection method according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a zero-crossing detection QRS method of a QRS complex identification method based on a differential zero-crossing detection method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a response curve of amplitude frequency and phase frequency of a filter of a QRS complex identification method based on a differential zero-crossing detection method according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an experimental result of a QRS complex identification method based on a differential zero-crossing detection method according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 4, for an embodiment of the present invention, a QRS complex identification method based on a differential zero-crossing detection method is provided, including:
s1: filtering the acquired original electrocardiosignals by using a self-adaptive baseline drift filter, and removing direct current bias of the original electrocardiosignals to obtain electrocardiosignals fluctuating up and down near a value of 0; it should be noted that, in the following description,
defining the time domain of the adaptive baseline shift filter includes:
y[n]＝x[n]-B[n]
where x [ n ] represents the filter input value, y [ n ] represents the filter output value, and B [ n ] represents the baseline wander of the fix-up.
Wherein the baseline wander of the repair comprises,
where W represents the window width.
The adaptive baseline shift filter further comprises: the window width is defined to be one tenth of the value of the sampling frequency.
Specifically, because the electrocardiosignals directly acquired by the high-precision analog front-end chip are very weak, the amplitude is usually between 0.1mV and 3mV, and three types of noise including baseline drift, power frequency interference and myoelectricity interference are accompanied, the QRS waves can be detected only by preprocessing. Wherein, power frequency interference belongs to low-frequency interference, and in the measurement process, analog front end circuit, measuring electrode and body surface have constituteed a voltage divider network, and four limbs action, breathing rhythm, the sweat of body surface etc. all probably influence body surface and electrode contact resistance change, and then cause the baseline drift. Such noise can distort the electrocardiographic waveform and seriously affect the detection of QRS complex, so filtering out baseline drift is a necessary preprocessing step.
The essence of the adaptive baseline shift filter is a digital low-pass filter, the window width of the filter needs to be set properly, if the window width is too narrow, the baseline shift with slow change is difficult to filter, and if the window width is too wide, the baseline shift with sudden change is difficult to filter. In addition, the filter needs to pre-sample data of a window width before outputting in consideration of stability of the output, and if the sampling frequency is 500Hz, the window width can be set to 50, and the pre-sampling time is 0.1 s. If the window width is set as "W", the filter time domain output equation is:
y[n]＝x[n]-B[n]
where x [ n ] represents the filter input value, y [ n ] represents the filter output value, and B [ n ] represents the baseline wander of the fix-up.
S2: filtering the electrocardiosignal by using a 15Hz Butterworth low-pass filter, filtering power frequency interference and inhibiting the influence of myoelectric interference on the signal; it should be noted that, in the following description,
the parameters defining an IIR Butterworth (15Hz Butterworth low pass filter) digital low pass filter are shown in table 1.
Table 1: a filter parameter table.
Specifically, the frequency spectrum of a signal acquired by a high-precision analog front-end chip is mainly concentrated between 0 and 60Hz, and the self-adaptive baseline shift filter only filters a baseline of an original signal, and the signal is still influenced by power frequency interference and electromyographic interference at the moment, wherein the power frequency interference is caused at the acquisition stage, the power line interference and the subharmonic interference of 50Hz are generally used domestically, the amplitude is about 0 to 0.4mV, and according to different conditions, the amplitude is generally equal to 5 to 40 percent of the amplitude of an R wave, which is a common interference which can be encountered by the commonly sampled signal, so that the SNR of the ECG signal is reduced, and even the original signal is submerged. The electromyographic interference frequency spectrum range is wide and is concentrated on 1-1000Hz, so that the signal is not smooth when viewed from the time domain and is mixed with peaks and burrs. In order to suppress these two types of noise, this embodiment employs an IIR Butterworth digital low-pass filter to filter frequency components above 15Hz, and the power frequency interference in the output signal is completely filtered, only the low-frequency component of the myoelectric interference remains, and the detection influence on the QRS complex is small.
S3: carrying out first-order and second-order difference on the electrocardiosignals after filtering processing, analyzing the probability distribution of numerical values after the first-order difference, and obtaining a threshold value for detecting an R peak; it should be noted that, in the following description,
in the electrocardiosignal, the modulus of the slope of the QRS complex is larger than that of other complexes, so that the first-order difference of the signal can effectively divide the QRS complex and other complexes, and then the QRS complex and other complexes are divided by using the first-order difference, wherein the first-order difference expression is as follows:
y[n]＝x[n]-x[n-1](n≥1)
adopting a power function as a mapping function to amplify a first-order difference result, namely a difference value, and distinguishing a QRS complex from other complexes, wherein the mapping function comprises the following steps:
y[n]＝(x[n])3
specifically, after first-order difference is performed on the filtered signal, a difference waveform corresponding to a QRS wave interval presents a positive peak and a negative trough, and a peak-trough module value is larger than other wave groups, but a module value difference is still small, and detection of an R peak is influenced to a certain extent. Jiapu Tompkins has proposed a Pan-Tompkins method, which amplifies the output after the difference through a square function, and detects by segmentation integration, so that whether a QRS complex exists can be roughly detected, but the QRS point cannot be accurately positioned.
The mapping function is:
y[n]＝(x[n])3
after the step, the wave crests and wave troughs corresponding to the QRS wave group can be obviously amplified.
S4: finding out S valley and Q valley in turn through second order difference, calculating heart rate and Q, R, S wave wavelength according to sampling frequency, extracting waveform characteristics, completing QRS wave group identification,
the R peak and the S valley comprise,
the zero crossing of the differential value between the time intervals of the wave crest and the wave trough is the time when the R peak appears in the electrocardiosignal, and the next zero crossing of the differential value is the time when the S trough appears in the electrocardiosignal.
Further, detecting and acquiring Q, R, S three points by using a zero-crossing detection algorithm includes:
setting a proper threshold value and window size, judging whether the difference value is the R point according to the threshold value if the difference value is changed from positive to negative, judging the difference value is the S point if the difference value is changed from negative to positive, and judging the point which is changed from negative to positive before the R peak appears as the Q point.
Specifically, in the differential waveform after the nonlinear mapping, it can be obviously observed that a peak and a trough exist in one heartbeat cycle, the zero-crossing of the differential value between the time intervals of the peak and the trough is the time when the R peak appears in the electrocardiographic signal, and the next zero-crossing of the differential value is the time when the S trough appears in the electrocardiographic signal. Since the differential waveform after the nonlinear mapping has obvious characteristics, the occurrence time of the R peak and the S valley can be detected by setting a threshold, and the nearest zero crossing before the R peak is the Q point, and the flow is shown in fig. 3. In one QRS identification, R, S points are identified successively, and the position of Q is finally determined, so that the invention can determine the characteristics and information of a QRS complex at the S point moment of the QRS complex, and can perform auxiliary diagnosis according to the characteristics of the QRS complex (such as the time interval between the three QRS points).
The QRS complex identification method based on the difference zero-crossing detection method has the characteristics of good real-time performance and small calculation amount, and can be used in an MCU with low calculation capacity.
Example 2
Referring to fig. 5, another embodiment of the present invention is shown, in order to verify and explain the technical effects adopted in the method, the embodiment adopts a simulation test and a comparison test between the conventional technical scheme and the method of the present invention, and compares the test results by means of scientific demonstration to verify the real effects of the method.
Firstly, a simulation test is carried out based on the method to verify the real effect of the method: the Texas instruments ADS1292R chip is selected as the front-end analog-to-digital conversion chip in the test, and the frequency of 500Hz is used for sampling in the actual test. The experiment is divided into a static test part and a dynamic test part, wherein in the static test, a tested person is in a standing static state; in the dynamic test, the test subject moves around in a room. Experiments prove that the method disclosed by the invention can effectively inhibit baseline drift and high-frequency noise in dynamic and static tests, obtain a better waveform and detect the position of a QRS point as shown in figure 5.
Then, the traditional technical scheme is adopted to carry out comparison test with the method of the invention, and the traditional technical scheme is as follows: at present, methods such as wavelet transformation, a neural network, template matching, a Teager energy operator and the like are provided for QRS waveform detection, the wavelet transformation has the advantage of low false detection rate, but real-time detection is difficult to achieve in a portable electrocardiogram monitor with low performance due to huge calculation amount, the neural network method is difficult to widely apply due to the fact that training period is long, and electrocardio waveforms with large differences with a training set are difficult to distinguish, template matching is easy to achieve, but the problem that waveforms with low correlations with templates are difficult to distinguish is also existed, the Teager energy operator distinguishes by tracking instantaneous energy changes of tracking signals, R peaks can be effectively detected, but the distinguishing of Q points and S points is slightly struggled. Compared with the methods, the QRS complex detection method based on the differential zero-crossing detection has the advantages of small calculation amount, good real-time performance and easiness in integration, and can be adopted in practical application. In order to verify that the method has high real-time performance, less calculation amount and higher detection accuracy compared with the traditional method, the traditional method for detecting the QRS complex of the wristband type electrocardiosignal based on the difference method and morphological calculation and the method are respectively used for measuring and comparing the electrocardiosignals in real time.
And (3) testing environment: selecting an ADS1292R chip of a Texas instrument as a front-end analog-to-digital conversion chip, sampling at 500Hz in actual test, connecting an electrode patch with a test sample of a tested person, and respectively testing by using a traditional QRS wave group detection method based on a difference method and morphological calculation and the method of the invention to obtain result data; starting automatic test equipment, programming by using MATLB software to realize simulation test of the two methods, and obtaining simulation data according to the experimental result. Each method was tested in 7 sets of data and the results are shown in the table below.
Table 2: and testing the data table based on a difference method and a method for detecting the QRS complex by morphological operation.
Table 3: experimental data sheet of the method of the invention.
| Test set data | Number of heartbeats | Number of false detections | Number of missed tests | False detection Rate (%) |
| 1 | 1774 | 0 | 0 | 0.0000％ |
| 2 | 2589 | 2 | 1 | 0.0772％ |
| 3 | 1985 | 2 | 0 | 0.1008％ |
| 4 | 2019 | 3 | 0 | 0.1486％ |
| 5 | 1855 | 1 | 1 | 0.0539％ |
| 6 | 1798 | 4 | 0 | 0.2225％ |
| 7 | 2330 | 3 | 2 | 0.1288％ |
As can be seen from the table above, compared with the traditional method, the method provided by the invention has the advantages of high real-time performance, small calculation amount and higher detection accuracy.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A QRS complex identification method based on a difference zero-crossing detection method is characterized by comprising the following steps:
filtering the acquired original electrocardiosignals by using a self-adaptive baseline drift filter, and removing direct current bias of the original electrocardiosignals to obtain electrocardiosignals fluctuating up and down near a value of 0;
filtering the electrocardiosignal by using a 15Hz Butterworth low-pass filter, filtering power frequency interference and inhibiting myoelectric interference;
carrying out first-order and second-order difference on the electrocardiosignals after filtering processing, analyzing the probability distribution of numerical values after the first-order difference, and obtaining a threshold value for detecting an R peak;
and sequentially finding an S valley and a Q valley through second-order difference, calculating the heart rate and the wavelength of Q, R, S waves according to the sampling frequency, extracting waveform characteristics and finishing the identification of the QRS wave group.
2. The QRS complex identification method based on differential zero-crossing detection method as claimed in claim 1, wherein: the time domain of the adaptive baseline shift filter includes,
y[n]＝x[n]-B[n]
where x [ n ] represents the filter input value, y [ n ] represents the filter output value, and B [ n ] represents the baseline wander of the fix-up.
3. A QRS complex identification method based on differential zero-crossing detection as claimed in claim 2, wherein: the baseline wander amount of the repair includes,
where W represents the window width.
4. The QRS complex identification method based on differential zero-crossing detection method as claimed in claim 1, wherein: segmenting QRS complexes and other complexes using the first order difference includes,
y[n]＝x[n]-x[n-1](n≥1)
5. a QRS complex identification method based on differential zero-crossing detection as claimed in claim 1 or 4, characterized in that: amplifying the first order difference result, i.e. the difference value, using a power function as a mapping function to distinguish the QRS complex from other complexes, wherein the mapping function comprises,
y[n]＝(x[n])3
6. the QRS complex identification method based on the differential zero-crossing detection method as claimed in claim 5, wherein: the R peak and the S valley include,
the zero crossing of the differential value between the time intervals of the wave crest and the wave trough is the time when the R peak appears in the electrocardiosignal, and the next zero crossing of the differential value is the time when the S trough appears in the electrocardiosignal.
7. The QRS complex identification method based on the differential zero-crossing detection method as claimed in claim 6, wherein: detecting the three points Q, R, S using a zero crossing detection algorithm includes,
setting a proper threshold value and a window size, if the difference value changes from positive to negative, judging whether the difference value is an R point according to the threshold value, if the difference value changes from negative to positive, judging the difference value is an S point, and judging the point which changes from negative to positive before the R peak appears as a Q point.
8. The QRS complex identification method based on the differential zero-crossing detection method as claimed in any one of claims 1 to 3, wherein: the adaptive baseline shift filter further includes a filter for filtering the signal,
the window width is defined as one tenth of the value of the sampling frequency.
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