Abstract
Myocardial infarction (MI) is an acute disease. Early detection and early treatment are of great significance for improving the health of people. In order to reduce the misdiagnosis rate of MI diseases, this paper proposes a multi-lead bidirectional gated recurrent unit neural network (ML-BiGRU) learning algorithm based on current research status in the field of intelligent medical diagnosis, combined with the timing and multi-lead correlation characteristics of the electrocardiogram (ECG) signals. At first, the original ECG signal is denoised and preprocessed and then segmented into heartbeats. After that, the heartbeat sequence is sent to the deep neural network training model to learn the classification. Lastly, the Physikalisch-Technische Bundesanstalt (PTB) ECG database is used to verify the multi-lead BiGRU algorithm. The verification results demonstrate that the accuracy of the algorithm for MI localization is 99.84%, which outperform the other algorithms. The experimental results also show that the algorithm is obviously superior to the traditional localization algorithm in improving the localization accuracy, which is of great significance for improving the correct diagnosis rate of MI.
Highlights
Cardiovascular disease is a serious threat to human health and is gradually becoming a high incidence in China
Where true positive (TP) is the number of heartbeats correctly detected as Myocardial infarction (MI), true negative (TN) is the number of heartbeats correctly identified as healthy control (HC), false negative (FN) is the number of heartbeats erroneously detected as HC, and false positive (FP) is the number of heartbeats erroneously diagnosed as MI
The ECG signal is preprocessed by the filter bank; the Pan-Tompkins algorithm is used to locate the R wave and each ECG signal is segmented into independent heartbeats; the BiGRU deep learning method is used to extract features automatically and implement the localization of MI
Summary
Based on the hand-crafted features from the ECG signal, researchers at home and abroad have proposed various automatic classification algorithms for MI to assist doctors in making a quick diagnosis. The traditional method of hand-crafted features uses the QRS complex detection algorithm to locate the R peak first. Classification of MI by automatic feature extraction from 8 leads ECG signal using deep learning framework. A neural network deep learning algorithm based on BiGRU and multi-lead ECG signals is proposed for the localization of MI.
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