Multi-lead electrocardiogram signal processing method and device, equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, is applied to the field of intelligent medical treatment, and discloses a multi-lead electrocardiogram signal processing method, device and equipment and a storage medium, which are used for improving the accuracy and richness of electrocardiogram signal extraction. The multi-lead electrocardiogram signal processing method comprises the following steps: acquiring a to-be-processed multi-lead electrocardiogram signal, wherein the to-be-processed multi-lead electrocardiogram signal is used for indicating heart detection information of a target object; performing data preprocessing on the to-be-processed multi-lead electrocardiogram signal to obtain processed electrocardiogram data; performing data framing processing on the processed electrocardiogram data to obtain multi-dimensional lead channel electrocardiogram data; and performing feature extraction and feature aggregation processing on the multi-dimensional lead channel electrocardiogram data through a deep neural network model fused with a double attention mechanism to obtain target electrocardiogram feature data. In addition, the invention further relates to a block chain technology, and the target electrocardiogram characteristic data can be stored in a block chain node.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence neural networks, in particular to a multi-lead electrocardiogram signal processing method, device, equipment and storage medium. Background technique [0002] Heart disease is one of the main culprits that threaten our health, and electrocardiogram is an important method for heart disease detection. ECG is a technology that the electrocardiograph records the changes of electrical activity generated by each cardiac cycle of the heart from the body surface. The electrocardiogram shows the health status of the heart rate, and the abnormal heart rate of ordinary users is detected by the electrocardiogram. [0003] In the field of medical artificial intelligence, automated ECG analysis methods mainly include manual feature extraction of typical waveforms and bands such as p-wave and qrs-wave, as well as feature extraction and ECG data classification of some deep learning classif...

AI Technical Summary

Problems solved by technology

At present, most of them use convolutional network (CNN) to train multi-lead ECG data to realize automatic analysis of ECG. Because the convolutional layer in CNN is limited by the receptive field, it leads to the limitation of long signal context information, and ignores the multi-channel of ECG signal. Correlation between channels, resulting in low accuracy of ECG signal extraction

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