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An Exhaustive Study on Deep Neural Network-based Prediction of Heart Diseases and its Interpretations



Jothiaruna Nagaraj1, Anny Leema A.1, *
1 Department of School of Information Technology Science and Engineering, Vellore Institute of Technology University, Vellore, Tamil Nadu 632014, India


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Creative Commons License
© 2022 Nagaraj and Leema A

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of School of Information Technology Science and Engineering, Vellore Institute of Technology University, Vellore, Tamil Nadu 632014, India; E-mail: annyleema.a@vit.ac.in


Abstract

Cardiovascular disease prediction is important in day-to-day life. A tool to diagnose cardiovascular diseases is an Electrocardiogram (ECG), which records electrical activities happening in the heart through a wave. A determination is made by checking the wave changes in an ECG. Predicting wave changes and diagnosing the disease requires domain expertise like cardiologists/physicians. Deep Neural Network techniques extract the features accurately and automatically predict the type of disease. This article lists different types of cardiac disorders, and parallelly different disease interpretations of all types of diseases are discussed to manually identify the disease type; segmentation of leads, pre-trained models, and different detection techniques are discussed to predict the type of diseases from an ECG image. Finally, this article discussed the different challenges in predicting heart diseases, and solutions to some of the challenges are given.

Keywords: Cardiovascular Disease, 12 Lead ECG, Deep Neural Network (DNN), Challenges, ECG image, Heart diseases.