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

K Manimekalai; Dr  A Kavitha

doi:10.17485/IJST/v13i28.445

Article

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

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i28.445

Year: 2020, Volume: 13, Issue: 28, Pages: 2823-2832

Original Article

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

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
College, Coimbatore

*Corresponding Author
Email: [email protected]

Received Date:27 March 2020, Accepted Date:27 April 2020, Published Date:07 August 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

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.

Keywords: Classification; CNN; deep learning; deep neural network; EDN; LSTM; Myocardial Infarction(MI)

References

Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al. Heart Disease and Stroke Statistics’2017 Update: A Report from the American Heart Association.
Baloglu UB, Talo M, Yildirim O, Tan RS, Acharya UR. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters. 2019;122:23–30. Available from: https://dx.doi.org/10.1016/j.patrec.2019.02.016
Liu W, Huang Q, Chang S, Wang H, He J. Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomedical Signal Processing and Control. 2018;45:22–32. Available from: https://dx.doi.org/10.1016/j.bspc.2018.05.013
Zhang X, Li R, Hu Q, Zhou B, Wang Z. A New Automatic Approach to Distinguish Myocardial Infarction Based on LSTM. In: 2019 8th International Symposium on Next Generation Electronics (ISNE). IEEE. .
Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine. 2019;25(1):65–69. Available from: https://dx.doi.org/10.1038/s41591-018-0268-3
Feng K, Pi X, Liu H, Sun K. Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network. Applied Sciences. 2019;9(9):1879. Available from: https://dx.doi.org/10.3390/app9091879
Rajkumar A, Ganesan M, Lavanya R. Arrhythmia classification on ECG using Deep Learning. In: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE. .
Darmawahyuni A, Nurmaini S, Sukemi W, Caesarendra V, Bhayyu. M Naufal Rachmatullah and Firdaus, Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier. Algorithms. 2019:118. Available from: https://doi.org/10.3390/a12060118
Shadmand S, Mashoufi B. A new personalized ECG signal classification algorithm using Block-based Neural Network and Particle Swarm Optimization. Biomedical Signal Processing and Control. 2016;25:12–23. Available from: https://dx.doi.org/10.1016/j.bspc.2015.10.008
Pal A, Garain U, Chandra A, Chatterjee R, Senapati S. Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network. Computer Methods and Programs in Biomedicine. 2018;159:59–69. Available from: https://dx.doi.org/10.1016/j.cmpb.2018.01.027
Baloglu UB, Talo M, Yildirim O, Tan RS, Acharya UR. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters. 2019;122:23–30. Available from: https://dx.doi.org/10.1016/j.patrec.2019.02.016
Fu L, Lu B, Nie B, Peng Z, Liu H, Pi X. Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals. Sensors. 2020;20(4):1020. Available from: https://dx.doi.org/10.3390/s20041020
Feng K, Pi X, Liu H, Sun K. Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network. Applied Sciences. 2019;9(9):1879. Available from: https://dx.doi.org/10.3390/app9091879
Ptb Diagnostic ECG Database. Available from: https://www.physionet.org/content/ptbdb/1.0.0/
Feng K, Pi X, Liu H, Sun K. Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network. Applied Sciences. 2019;9(9):1879. Available from: https://dx.doi.org/10.3390/app9091879

Copyright

© 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).