Electrocardiogram intelligent analysis method and system based on deep neural network

Abstract

The invention discloses an electrocardiogram (ECG) intelligent analysis method and system based on a deep neural network. The method comprises a training stage and a detection stage; the training stage comprises the following steps: annotating a horizontal layer label and a longitudinal layer label on image information of each collected N-channel static ECG; training to obtain N convolutional neural network models for identifying the horizontal layer and convolutional neural network models for identifying the longitudinal layer; the detection stage comprises the following steps: regarding theN feature sequences of the collected to-be-detected N-channel static ECG and the feature sequence segmented by taking the heartbeat period as the input of N conventional neural network models for identifying the horizontal layer and the convolutional neural network models for identifying the longitudinal layer, thereby obtaining the horizontal identification abnormal analysis and the longitudinalidentification abnormal analysis. All channels are respectively learned and judged by using the convolutional neural networks; experimental results show that the method has better identification effect. The method disclosed by the invention is stronger in operability, better in network generalization capacity, and high in ECG correct identification rate.

Description

technical field [0001] The present invention relates to the technical field of medical artificial intelligence, in particular to a deep neural network-based intelligent electrocardiogram analysis method and system. Background technique [0002] According to an authoritative survey, cardiovascular disease has become one of the main causes of death in the world. Cardiovascular diseases account for one-third of all deaths each year. [0003] The electrocardiogram is a common medical examination method used to observe the electrical activity of the human heart. The electrocardiogram machine extracts the electrical signal of the heart activity into a digital signal and displays it in the form of an electrocardiogram. With the development of artificial intelligence, especially deep learning technology, the technology for analyzing digital ECG signals is becoming more and more abundant and mature. However, manual analysis of ECG not only requires a large amount of labor by profes...

AI Technical Summary

Problems solved by technology

[0005] In view of the technical problems in the prior art that it is difficult to extract the features of the static electrocardiogram and the judgment and recognition rate is low, the present invention proposes an intelligent analysis method and system for the electrocardiogram based on a deep neural network. Accurate judgment of 12 or 18-lead ECG, through the overall horizontal judgment of 12 or 18 leads and the longitudinal judgment of a single cardiac cycle, the rhythm and shape of the patient's ECG can be identified

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