Deep Learning Methods in Classification of Myocardial Infarction by employing ECG Signals

Abstract

Background/Objectives: To automatically classify and detect the Myocardial Infarction using ECG signals. Methods/Statistical analysis: Deep Learning algorithms Convolutional Neural Network(CNN), Long Short Term Memory(LSTM) and Enhanced Deep Neural Network(EDN) were implemented. The proposed model EDN, comprises the techniques CNN and LSTM. Vector operations like matrix multiplication and gradient decent were applied to large matrices of data that are executed in parallel with GPU support. Because of parallelism EDN faster the execution time of process. Findings: Proposed model EDN yields better accuracy (88.89%) than other state-of-art methods for PTB database. Novelty/Applications: The proposed classification algorithm for analyzing the ECG signals is obtained by comprising the Convolutional Neural Network(CNN)and Long short-term memory networks(LSTM). Also, it is identified that the novel classification technique based on deep learning decreases the misdiagnosis rate of MI.