CardioNet: A Lightweight Deep Learning Framework for Screening of Myocardial Infarction Using ECG Sensor Data

Abstract

Myocardial infarction (MI) stands as one of the most critical cardiac complications, occurring when blood flow to the cardiovascular system is partially or completely blocked. Electrocardiography (ECG) is an invaluable tool for detecting diverse cardiac irregularities. Manual investigation of MI-induced ECG changes is tedious, laborious, and time-consuming. Nowadays, deep learning-based algorithms are widely investigated to detect various cardiac abnormalities and enhance the performance of medical diagnostic systems. Therefore, this work presents a lightweight deep learning framework (CardioNet) for MI detection using ECG signals. To construct time-frequency (T-F) spectrograms, filtered ECG sensor data are subjected to the short-time Fourier transform (STFT), movable Gaussian window-based S-transform (ST), and smoothed pseudo-Wigner-Ville distribution (SPWVD) methods. To develop an automated MI detection system, obtained spectrograms are fed to benchmark Squeeze-Net, Alex-Net, and a newly developed, lightweight deep learning model. The developed CardioNet with ST-based T-F images has obtained an average classification accuracy of 99.82%, a specificity of 99.57%, and a sensitivity of 99.97%. The proposed system, in combination with a cloud-based algorithm, is suitable for designing wearable to detect several cardiac diseases using other biological signals from the cardiovascular system.