Miocardial infarction automatic detection method based on multimodality fused neural network

Abstract

The invention discloses a miocardial infarction automatic detection method based on multimodality fused neural network. The method comprises the steps of (1) cutting off single heart beat, and generating 12 linked electrocardiosignal samples; (2) building a convolution neural network model of the 12 linked electrocardiosignal; (3) training parameters of the convolution neural network; and (4) automatically identifying the tested samples, inputting the divided test samples into the convolution neural network, performing operating to obtain a two-dimensional predicted value vector correspondingto the test samples, performing outputting, generating two-dimensional label vectors for the label of the test samples by a one-hot coding method, comparing the output predicted value with the label of the test samples to inspect whether classification is right or not, and identifying the performance of the model through the classification result y_pred. The method can obtain high accuracy rate for multiple linking electrocardiosignal, wherein the distinguishing accuracy rate of miocardial infarction heart beat can achieve 99.51%.

Description

technical field [0001] The invention relates to the technical field of medical signal processing, more precisely, an automatic detection method for myocardial infarction based on a multimodal fusion neural network. Background technique [0002] With the development of digital technology, computer-aided diagnosis system has become the most promising solution for clinical diagnosis due to its fast and reliable analysis means. Today, through advanced hardware facilities, we can easily obtain the patient's ECG signal, which is what people call an ECG. Physicians can judge the patient's state by observing the information contained in the ECG, however, the process of manually or visually inferring these subtle morphological changes in long continuous ECG beats is time-consuming and prone to errors due to fatigue. Therefore, real-time computer-aided diagnosis systems are essential to help physicians monitor patients' conditions in real time and overcome these limitations in the ev...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the traditional machine learning framework to solve the problem that the electrocardiographic signal will change due to the pathological changes of the information management sy

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