Localization of myocardial infarction with multi-lead bidirectional gated recurrent unit neural network

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.