Indian Journal of Science and Technology
Year: 2020, Volume: 13, Issue: 28, Pages: 2823-2832
K Manimekalai1,2*, Dr. A Kavitha3
1Research Scholar, Department of Computer Science, Kongunadu Arts and Science College, Coimbatore
2Assistant Professor, Department of Computer Applications, Sri GVG Visalakshi College For Women, Udumalpet
3Associate Professor, Department of Computer Science, Kongunadu Arts and Science
Email: [email protected]
Received Date:27 March 2020, Accepted Date:27 April 2020, Published Date:07 August 2020
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.
Keywords: Classification; CNN; deep learning; deep neural network; EDN; LSTM; Myocardial Infarction(MI)
© 2020 Manimekalai &Kavitha.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).
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