Method, device, electronic equipment and storage medium for electrocardiogram signal detection and classification

A technology for electrocardiographic signals and classification methods, which is applied in the directions of diagnostic recording/measurement, medical science, diagnosis, etc., and can solve the problems of wrong identification results, difficult detection of waveform characteristics, and wrong classification of atrial fibrillation.

Active Publication Date: 2019-09-13
GUANGZHOU SHIYUAN ELECTRONICS CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the P wave or f wave in the ECG signal is a weak signal, and its waveform characteristics are difficult to detect
Moreover, many non-AF types of abnormal rhythms (such as tachycardia, bradycardia, arrhythmia, etc.) show characteristics si

Method used

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  • Method, device, electronic equipment and storage medium for electrocardiogram signal detection and classification
  • Method, device, electronic equipment and storage medium for electrocardiogram signal detection and classification
  • Method, device, electronic equipment and storage medium for electrocardiogram signal detection and classification

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Embodiment 1

[0130] figure 1 It is a flowchart of a method for detecting and classifying ECG signals provided in Embodiment 1 of the present application. The method for detecting and classifying ECG signals may specifically include the following steps:

[0131] Step S110, extract the signal waveform from the electrocardiographic signal.

[0132] In a specific implementation, multi-channel synchronous data can be used to collect human heart signals, background noise and electrocardiographic signals. More specifically, firstly, ECG signals can be collected through ECG leads and sensors, and the collected ECG signals can be subjected to impedance matching, filtering, amplification and other processing through analog circuits. Then, the analog signal of the physiological parameters of the human body is converted into a digital signal by an analog-to-digital converter. Then, the filtered ECG signal is obtained through a low-pass filtering technique. Finally, the signal waveform is extracted ...

Embodiment 2

[0325] Figure 8 It is a schematic structural diagram of an electrocardiographic signal detection and classification device provided in Embodiment 2 of the present application. refer to Figure 8 , The ECG signal detection and classification device provided in this embodiment specifically includes: a waveform extraction module 810, a feature acquisition module 820, a feature input module 830 and a classification module 840, wherein:

[0326] The waveform extraction module 810 is used to extract the signal waveform from the ECG signal;

[0327] The feature acquisition module 820 is used to acquire the morphological features and deep features of the signal waveform; the morphological features include any one of TR wave amplitude difference features, PR wave quantity ratio features, ST band features, and P wave change features; The deep features include deep features and hierarchical features;

[0328] A feature input module 830, configured to input the morphological features ...

Embodiment 3

[0358] Figure 9 It is a schematic structural diagram of an electronic device provided in Embodiment 3 of the present application. As shown in the figure, the electronic device includes: a processor 90 , a memory 91 , a display screen 92 with a touch function, an input device 93 , an output device 94 and a communication device 95 . The number of processors 90 in the electronic device may be one or more, and one processor 90 is taken as an example in the figure. The number of memory 91 in the electronic device may be one or more, one memory 91 is taken as an example in the figure. The processor 90 , memory 91 , display screen 92 , input device 93 , output device 94 and communication device 95 of the electronic device can be connected via a bus or in other ways. In the figure, connection via a bus is taken as an example. In an embodiment, the electronic device may be a computer, a mobile phone, a tablet, a projector or an interactive smart tablet, and the like. In the embodim...

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PUM

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Abstract

The invention relates to a method, a device, electronic equipment and a storage medium for electrocardiogram signal detection and classification. The method comprises the following steps of: extracting a signal waveform from an electrocardiogram signal; acquiring a morphological characteristic and a deep characteristic of the signal waveform, wherein the morphological characteristic comprises anyone of a TR wave amplitude difference characteristic, a PR wave quantity ratio characteristic, an ST wave band characteristic and a P wave variation characteristic, and the deep characteristic comprises a depth characteristic and a hierarchy characteristic; inputting the morphological characteristic and the deep characteristic into a classifier; and acquiring a classification result output by theclassifier so as to obtain the signal type of the electrocardiogram signal, wherein the classification result is a result that the classifier performs classification according to the morphological characteristic and the deep characteristic, and the signal type comprises an atrial fibrillation rhythm, a non-atrial fibrillation abnormal rhythm, a normal sinus rhythm and noise. The method can accurately identify various types of abnormal rhythms, avoids the situation that abnormal rhythms of non-atrial fibrillation types such as tachycardia, bradycardia and arrhythmia are classified into atrial fibrillation types in error, and improves the accuracy of classification of electrocardiogram signals.

Description

technical field [0001] The present application relates to the field of medical devices and medical products, in particular to a method, device, electronic equipment and storage medium for detecting and classifying electrocardiographic signals. Background technique [0002] Atrial fibrillation (AF), referred to as atrial fibrillation, is the most common clinical arrhythmia disease, which is characterized by disordered atrial activity and subsequent complications such as stroke and myocardial infarction, resulting in a high risk of death. Disability and mortality seriously endanger human health and life. The algorithm to study whether there is atrial fibrillation in the ECG signal can detect and treat it early, so as to seize the best time for treatment and reduce the morbidity and mortality of atrial fibrillation, so it has important clinical and social significance. [0003] Since the two important clinical manifestations of atrial fibrillation are absolute irregularity of ...

Claims

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Application Information

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IPC IPC(8): A61B5/00A61B5/04A61B5/0452A61B5/046A61B5/361
CPCA61B5/7267A61B5/316A61B5/349A61B5/361
Inventor 胡静赵巍
Owner GUANGZHOU SHIYUAN ELECTRONICS CO LTD
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