Detection Of Cardiac Arrhythmias Using Machine Learning

Abstract

An irregular heartbeat caused by anomalies in the electrical conduction of the heart muscle is known as a cardiac arrhythmia.     ECG devices are used to noninvasively monitor and diagnose cardiac arrhythmias in clinical settings.     Visual inspection and analysis of ECG signals are difficult and time-consuming due to their dynamic nature and the abundance of complex information they convey.  Therefore, an automated system that can distinguish between abnormal and normal ECG signals is required to assist doctors in quickly and reliably identifying cardiac arrhythmias. The primary objective of this work is to use transfer learning algorithms and a Morse-based time-frequency representation to improve the diagnosis of cardiac arrhythmias from ECG data.  A CNN—LSTM hybrid deep learning model was shown to identify cardiac arrhythmias using CWT images of ECG data.    The suggested method's accuracy for ARR, CHF, and NSR was 98.0%, 96.0%, and 98.0%, respectively.