Electrocardiosignal classification method and device

Abstract

The invention relates to the technical field of electrocardiogram analysis, in particular to an electrocardiosignal classification method and device. The electrocardiosignal classification method includes the steps: a, segmenting extracted electrocardiosignals to obtain standard input data at a training stage; b, building a deep neural network through the standard input data at the training stage and training the deep neural network to obtain an electrocardiosignal classifier; c, converting the acquired original electrocardiosignals into data in a format as same as that of the standard input data at the training stage to obtain standard input data at an application stage, and inputting the standard input data at the application stage to the electrocardiosignal classifier for electrocardiosignal classification. A large number of signal processing steps based on field knowledge are omitted, and adaptive learning can be effectively achieved according to electrocardiogram data acquired by different equipment and different users in different environments.

Description

technical field [0001] The invention relates to the technical field of electrocardiographic analysis, in particular to a method and device for classifying electrocardiographic signals. Background technique [0002] Heart health is an important indicator of human health, and ECG signal analysis is an important link in the process of quantifying human health. An electrocardiogram is a visual time series that records the electrical activity of the human heart, and is widely used clinically to detect heart conditions. Its working principle is that the depolarization process of cardiomyocytes with each heartbeat will cause small spots of blood changes on the surface of the skin, which can be captured and recorded by the electrocardiogram recording device. The ECG signal is the data representation of the ECG. [0003] In addition to the signal wave triggered by the heartbeat, the ECG signal also contains a variety of interference signals, such as 33Hz myoelectric clutter generat...

AI Technical Summary

Problems solved by technology

[0007] The present invention provides a method and device for classifying electrocardiographic signals, aiming to solve the technical problems that the classification process of the existing electrocardiographic signal classification method is complicated, the classification learning process relies on the results of pre-filtering processing, and the classification effect of dynamic electrocardiographic signals is not good.

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