Electrocardiosignal feature point detection method and system

Abstract

The invention discloses an electrocardiosignal feature point detection method and system. The electrocardiosignal feature point detection method comprises the following steps: obtaining electrocardiosignals in continuous time; segmenting the electrocardiosignals in the continuous time to obtain standard electrocardiosignal segments; and extracting electrocardiosignal features are extracted from the standard electrocardiosignal segments based on a constructed one-dimensional coding and decoding deep learning model, carrying out waveform classification according to the electrocardiosignal features, and obtaining the detection result of each type of waveform boundary feature points according to the obtained waveform classification result. The electrocardiosignal features are automatically extracted by using the deep learning method, and the waveform classification of P waves, QRS waves, T waves and non-wavebands and detection of the boundary points of the P waves, the QRS wave groups and the T waves can be realized at the same time.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence data analysis, in particular to a method and system for detecting feature points of electrocardiographic signals. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The electrocardiogram captures the propagation process of electrical signals in the heart from the body surface, and displays the pathological state of the ventricles and atria through changes in waveform or rhythm. This type of equipment can perform real-time non-static ECG monitoring, but long-term ECG monitoring will generate a large number of ECG data. A normal cardiac cycle is usually composed of P wave, QRS wave group and T wave. The analysis of ECG is first to find the QRS wave group, because the QRS wave group is the most prominent feature in the ECG and is the basis for the det...

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Problems solved by technology

[0004] The main method of QRS complex detection is to enhance the QRS of the ECG or weaken the intensity of the remaining waveforms according to the basic laws of the ECG signal and cardiac electrophysiological activities, and set the threshold for detection; The first method is the fixed window search method and the QRS-T elimination method, but it is difficult for the above traditional methods to take all situations into account. When there is an ECG signal outside of consideration, the waveform detection accuracy is seriously reduced, and the P wave and T wave The wave shape is variable and may show biphasic morphology or absent state, which makes P wave and T wave detection a major difficulty

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