Electrocardiogram profiling of myocardial infarction history using MLP and HMLP networks

• Published in: Indonesian Journal of Electrical Engineering and Computer Science
• Main Author: Gani F.S.A.; Nordin M.K.; Yassin A.I.M.; Ibrahim I.P.; Megat Ali M.S.A.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2019
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073821123&doi=10.11591%2fijeecs.v17.i1.pp183-190&partnerID=40&md5=3b94f41d7b37d0312b8e320e9d674097

Narrowing of coronary arteries caused by cholesterol deposits deprives heart tissues of oxygen. In prolonged conditions, these will result in myocardium infarction. The presence of damage tissues modifies the normal sinus rhythm and this can be detected using electrocardiogram (ECG). Hence, this paper characterized history of myocardial infarction from survivors using QRS power ratio features from the ECG. Subsequent profiling is performed using multilayered perceptron (MLP) and hybrid multilayered perceptron (HMLP) networks. ECG with history of anterior and inferior infarctions, along with healthy controls is obtained from PTB Diagnostic ECG Database. The signal is initially pre-processed and the power ratio features are extracted for low- and mid-frequency components. The features are then used as input vector to the MLP and HMLP networks. The optimized MLP has attained accuracies of 99.2% for training and 98.0% for testing. Meanwhile, the optimized HMLP managed to achieve accuracies of 99.4% for training and 97.8% for testing. Despite the similarities in network performance, MLP provides a better alternative due to the reduced computational requirements by as much as 30%. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.