Adapting Transfer Learning for Accurate ECG-Based Heart Disease Classification

Sania Urooj ()
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Sania Urooj: Department of Artificial Intelligence, FAST National University, Karachi, Sindh, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6 Special Issue: 7, issue 7, 246-257

Abstract: ECG signals are widely used for analyzing heart rhythms and detecting abnormalities. This study presents an experimental evaluation of a Deep CNN model for classifying ECG scalograms. Using publicly available datasets containing records from 242 patients, the study aims to classify three different cardiovascular diseases: Congestive Heart Failure (CHF), Myocardial Infarction (MI), and Coronary Artery Disease (CAD). The raw ECG signals undergo several preprocessing steps, including up-sampling, removal of noise and artifacts, and conversion into 2D images. Continuous Wavelet Transform (CWT) is applied to represent the ECG signals as 2D scalograms. The experiments in this work are conducted using a Deep CNN model and the pre-trained Inception V3model, which achieved accuracies of 96.87% and 90.11%, respectively, on the CWT scalograms of the ECG datasets. The results were thoroughly analyzed, and the model’s performance was compared with other existing studies in the field.

Keywords: Cardiac Heart Disease; Electrocardiogram; Convolution Neural Network; Transfer Learning (search for similar items in EconPapers)
Date: 2024
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