AN APPROACH TO COMPUTER-AIDED DIAGNOSIS OF HEART DISORDERS USING WAVELETS AND DEEP LEARNING APPLIED TO ELECTROCARDIOGRAMS (EKGS)

Abstract

Purpose: The purpose of this study was to evaluate the potential of deep learning as a tool for computer-aided diagnosis of heart disorders based on EKG signals, using wavelet transformations to generate images. The research question was whether deep learning algorithms could accurately diagnose heart disorders and provide a valuable complement to traditional EKG views. Methods: We trained five Convolutional Neural Networks (CNNs) using EKG data obtained from the Physionet public database. The algorithms were developed using MATLAB version 2018b and the toolboxes for digital signal processing, neural networks, and wavelets. We evaluated the performance of the CNNs using accuracy, sensitivity, specificity, positive predictive value, and negative predictive value as metrics. Results: The CNNs demonstrated accuracy greater than 90%, and achieved good performance for the other evaluated parameters. We also identified that the representation of EKGs as scalograms showed potential for use as a complement to traditional EKG views. Conclusion: Our findings demonstrate that deep learning is a promising tool for diagnosing heart disorders based on EKG signals, and can be a valuable complement to traditional EKG views. While automated diagnoses should not replace clinical judgment, deep learning can provide additional support to healthcare professionals. Further research should explore the potential of deep learning for medical diagnosis and the use of scalograms as a complementary tool in clinical practice.