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Method of detecting QRS complex start point in multi-lead electrocardiogram

A technology of QRS complexes and detection methods, which is applied in diagnostic recording/measurement, medical science, sensors, etc., can solve the problems of irregular RR interval, strong algorithm dependence, and large amount of calculation of wavelet transform method, and achieve real-time detection Strong performance, high detection accuracy and simple algorithm

Active Publication Date: 2019-10-25
FUDAN UNIV
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Problems solved by technology

[0003] The template matching method needs to make corresponding adjustments according to the differences of different individuals. The algorithm is highly dependent and susceptible to noise interference, and the detection results are often unsatisfactory.
When arrhythmia occurs, especially premature ventricular contractions, the amplitude of the R wave of the QRS complex changes greatly, and the RR interval is irregular, and the traditional differential threshold method cannot be accurately located
Although the method of artificial neural network has high adaptability and good detection effect, the realization of this method requires a large number of representative samples, which is difficult to realize in the actual application process.
The wavelet transform method has a large amount of calculation, complicated steps, and long time-consuming, and it is impossible to locate the start and end points

Method used

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Embodiment 1

[0037] Embodiment 1: The method for detecting the starting point of the QRS complex of the present invention is applied to the sinus rhythm signal (the rhythm of different parts is consistent). In this embodiment, the body surface 12-lead electrocardiogram (that is, the number of leads n=12) with a sampling rate of 1000 Hz is used, and the workflow is as follows:

[0038] (1) Observe the body surface 12-lead electrocardiographic signal (digital signal) of the sinus rhythm that is collected synchronously, such as figure 2 (a) shown. The light gray horizontal line for each lead represents its zero potential line. It can be seen from the figure that the ECG signals of some leads have serious baseline drift, such as I, II, III, aVR, aVL, aVF, V2, V3, V4, V5, and V6.

[0039](2) Preprocessing the body surface 12-lead electrocardiographic signals in sinus rhythm in step (1). Firstly, the signal is decomposed into 10 layers by using the sym4 wavelet basis function. For the 1000Hz...

Embodiment 2

[0044] Embodiment 2: apply the detection method of QRS wave group starting point of the present invention to premature ventricular beat signal (before sinoatrial node impulse has not yet arrived at ventricle, any position in the ventricle or the ectopic rhythm point of the interventricular septum sends out electric impulse in advance , causing depolarization of the ventricles). In this embodiment, the body surface 12-lead electrocardiogram (that is, the number of leads n=12) with a sampling rate of 1000 Hz is used, and the workflow is as follows:

[0045] (1) Observe the body surface 12-lead electrocardiographic signal (digital signal) of polymorphic ventricular premature beats that is collected synchronously, such as image 3 (a) shown. The light gray horizontal line of each lead indicates its zero potential line. It can be seen from the figure that there are serious baseline drifts in the ECG signals of some leads, such as II, III, aVR, aVL, AVF, V1, V3, V4, V5 , V6.

[0...

Embodiment 3

[0051] Embodiment 3: The method for detecting the starting point of the QRS complex of the present invention is applied to the signal of atrial fibrillation (there are multiple rhythms, and the rhythms in different parts may be inconsistent). In this embodiment, the body surface 12-lead electrocardiogram (that is, the number of leads n=12) with a sampling rate of 1000 Hz is used, and the workflow is as follows:

[0052] (1) observe the body surface 12-lead electrocardiographic signal (digital signal) of atrial fibrillation collected synchronously, such as Figure 4 (a) shown. The light gray horizontal line of each lead represents its zero potential line. It can be seen that the ECG signals of some leads have serious baseline drift, such as I, II, aVR, aVL, V3, V5, and V6.

[0053] (2) Preprocessing the 12-lead electrocardiographic signal of atrial fibrillation in step (1). Firstly, the signal is decomposed into 10 layers by using the sym4 wavelet basis function. For the 1000...

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Abstract

The invention relates to a method of detecting a QRS complex start point in multi-lead electrocardiogram. The method comprises: acquiring multi-lead electrocardiogram by an electrocardiogram acquisition technique; pretreating the multi-lead electrocardiogram to obtain a signal with baseline drift and high-frequency noise removed; normalizing the signal to obtain normalized multi-lead electrocardiogram; calculating root-mean-square RMS of the normalized multi-lead electrocardiogram, detecting a function DEF, calculating to obtain a threshold by learning values of RMS and DEF several seconds before, and considering a DEF peak that meets the threshold as a QRS complex start point. The method has the advantages of high detection accuracy, good algorithm simplicity, good operation timeliness and the like. The method is applicable to long-range or short-range electrocardiograms, is suitable for analyzing sinus rhythm signals or arrhythmia signals, and is worthy of application in the researchon electrophysiologic mechanism and in clinical medicine. The method is popularizable to the analysis of intracardiac electrical signal activation and to related quantitative researches.

Description

technical field [0001] The invention relates to a method for detecting the starting point of the QRS wave group of multi-lead electrocardiographic signals. Background technique [0002] In the ECG cycle signal, the QRS wave group has the largest amplitude and the most obvious characteristics. Therefore, in the existing ECG signal feature point detection algorithm, there are many detection methods related to the QRS wave group. The common methods are: template matching method, difference Threshold method, artificial neural network method and wavelet transform method. [0003] The template matching method needs to make corresponding adjustments according to the differences of different individuals. The algorithm is highly dependent and susceptible to noise interference, and the detection results are often unsatisfactory. When arrhythmia occurs, especially premature ventricular contractions, the amplitude of the R wave of the QRS complex changes greatly, and the RR interval is...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0472A61B5/00A61B5/366
CPCA61B5/7203A61B5/7235A61B5/316A61B5/366
Inventor 杨翠微何凯悦陈家曦丁小曼
Owner FUDAN UNIV
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