Automatic detection method of myocardial infarction based on lead fusion deep neural network

Abstract

The invention discloses an automatic detection method of myocardial infarction based on lead fusion deep neural network. The automatic detection method includes the following steps: 1) generating 12-lead ECG signal samples by intercepting a single heart beat; 2) building a 12-lead ECG signal cnvolutional neural network model; 3) training parameters of the convolutional neural network; and (4) carrying out automatic recognition on test set samples; and inputting the divided test set samples into the convolutional neural network and carrying out running to obtain the 2-dimensional predicted value vector output corresponding to the test set samples, generating the 2-dimensional label vector using the one-hot coding method for labels of the test set samples, comparing the output predicted values with the labels of the test set samples to check the correctness of the classification, and judging the performance of the model by the classification result y_pred. The method achieves relativelyhigh accuracy in the recognition of multi-lead ECG signals. furthermore, the accuracy of the method in the recognition of heart beat 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 detection method for myocardial infarction based on a lead fusion deep 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, it is easy to obtain the patient's ECG signal, which is also known as the 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 evalu...

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 method for myocardial infarction based on lead fusion deep neural network

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