Myocardial infarction automatic detection system based on multimodal fusion neural network

Abstract

The invention discloses an automatic detection system for myocardial infarction based on a multi-modal fusion neural network, which includes: 1) generating a 12-lead ECG signal sample by intercepting a single cardiac beat; 2) constructing a 12-lead cardiac signal sample Convolutional neural network model of electrical signals; 3) Training parameters of convolutional neural network; 4) Automatic identification of test set samples; input the divided test set samples into convolutional neural network and run to obtain test set samples The corresponding 2-dimensional predicted value vector output, the label of the test set sample is generated using the one-hot encoding method to generate a 2-dimensional label vector, and the output predicted value is compared with the label of the test set sample to check whether the classification is correct. The result y_pred is used to judge the performance of the model. This method has achieved a higher accuracy rate for the identification of multi-lead ECG signals. Among them, the accuracy rate of heartbeat recognition for myocardial infarction can reach 99.51%.

Description

technical field [0001] The invention relates to the technical field of medical signal processing, more precisely, an automatic myocardial infarction detection system 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 evalua...

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 system and some external factors such as the age and gender of the patient, and the problem of weak generalization ability, while Provide an automatic detection system for myocardial infarction based on multimodal fusion neural network

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