Recurrent Ascendancy Feature Subset Training Model using Deep CNN Model for ECG based Arrhythmia Classification

Abstract

The World Health Organization (WHO) has released a report warning of the worldwide epidemic of heart disease, which is reaching worrisome proportions among adults aged 40 and high. Heart problems can be detected and diagnosed by a variety of methods and procedures. Scientists are striving to find multiple approaches that meet the required accuracy standards. Finding the heart issue in the waveform is what an Electrocardiogram (ECG) is all about. Feature-based deep learning algorithms have been essential in the medical sciences for decades, centralising data in the cloud and making it available to researchers around the world. To promptly detect irregularities in the cardiac rhythm, manual analysis of the ECG signal is insufficient. ECGs play a crucial role in the evaluation of cardiac arrhythmias in the context of daily clinical practice. In this research, a deep learning-based Convolution Neural Network (CNN) framework is adapted from its original classification task to automatically diagnose arrhythmias in ECGs. A deep convolution network that has been used for training with most relevant feature subset is used for accurate classification. The primary goal of this research is to classify arrhythmia using a deep learning method that is straightforward, accurate, and easily deployable. This research proposes a Recurrent Ascendancy Feature Subset Training model using Deep CNN model for arrhythmia Classification (RAFST-DCNN-AC). The suggested framework is tested on ECG waveform circumstances taken from the MIT-BIH arrhythmia database. The proposed model when contrasted with the existing models exhibit better classification rate.