Analysis of Deep Learning Approaches to Predict Cardiac Arrhythmia in ECG Signals

Abnormal heart rhythm or irregular heartbeat, often known as arrhythmia. It is a kind of cardiovascular illness that necessitates a precise and fast diagnosis. Because of its simplicity and non-invasive nature, an electrocardiogram (ECG) that detects the electric activity of the heart has been frequently used to identify cardiac disorders. Each heartbeat's electrical signal, the peak of action impulse waveforms produced by various specialised cardiac tissues, can be used to diagnose various heart defects. Deep learning has evolved as a significant technique in recent decades due to its ability to handle vast amounts of data. The use of hidden layers in the convolution layer have enhanced pattern recognition performance. Deep learning has aided in the automation of medical image analysis and can aid in detecting of any anomalies in the medical images. In this work, ECG-based automated irregular heartbeat prediction is conducted to determine to which arrhythmia class it belongs with greater accuracy and less data loss. This study is based on convolutional neural networks, which are used to evaluate ECG images. For the normal case and cases impacted by various arrhythmias and my-ocardial infarction, the signals correspond to electrocardiogram (ECG) forms of heartbeats.lD-CNN, ResNet34, ResNet50, vgg16, and vgg19 models are utilised to predict of cardiac arrhythmia. vgg16 performed the best and is chosen to be further tweaked to improve accuracy to 99.79 percent.

I. Introduction

Cardiovascular disease(CVD) is used to refer to several ailments that have an impact on the heart and blood vessels. Heart failure, stroke, peripheral artery disease, and coronary artery disease (CAD) are a few examples of disorders that might be classified as CVD. According to the World Health Organisation, CVD is one of the major reasons for fatalities today. Approximately 17.7 million individuals worldwide, or nearly 31% of all deaths, died from CVDs in 2017. More than 75% of these deaths took place in low- and middle-income countries [20].