Detection of Arrhythmia in ECG signal using Deep Learning Methods – A exhaustive review & summary of the concepts & techniques

Abstract

In this research article, the review of detection of arrhythmia in ECG signal using deep learning methods is presented in a nutshell. In today’s world one of the serious health problem is heart disease. Arrhythmia is the ailment in which the heart rhythm is irregular. It is may not be life threatening; still, it may lead to heart failure or attack if not taken care of it in time. Different examinations and measures are offered, which help to analyze arrhythmia. These comprise of blood tests, cardiac catheterization, chest X-ray, echocardiography (echo), electrocardiography (ECG), ultrasound, Holter monitor, etc. Of all these tests, ECG is the utmost commonly used for the identification of arrhythmia. Simple hardware systems can be used, ECG data can be examined. This benefits in systematizing the analysis of arrhythmia.  The objective of our research is to design a deep learning method for effective and quick classification of cardiac arrthymias. The automatic recognition of uncharacteristic heartbeats from a huge amount of data is a necessary and significant process. The ECG signals is taken from many database set (all classes of arrthymias). Each and every ECG beat will be obtained by using Discrete Wavelet transform i.e. 2D image which will be the input to Neural Networks. This is followed by Deep Learning methods that are used to teach a deep neural network based classifier (GA) to identify arrthymias. It will be implemented in the real-time scenario so patient condition can be recognized immediately without any delay and the treatment can be started by doctor without any second thought.  Our research work is efficient, quick (real-time classification) and simple to use.

Arrhythmia is a common cardiac disorder that can have serious health implications if not detected and treated in a timely manner. This abstract presents a comprehensive review and summary of the concepts and techniques related to the detection of arrhythmia in electrocardiogram (ECG) signals using deep learning methods. With the increasing availability of large ECG datasets and advancements in deep learning, the application of artificial intelligence in arrhythmia detection has gained significant attention. This review explores the fundamental concepts of ECG signal analysis, the challenges associated with arrhythmia detection, and the recent developments in deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for accurate arrhythmia classification. Various pre-processing methods, feature extraction techniques, and model architectures are discussed, highlighting their respective advantages and limitations. The paper also examines the performance metrics and datasets commonly used in this domain. By synthesizing the latest research findings and methodologies, this review serves as a valuable resource for researchers and practitioners aiming to advance the field of arrhythmia detection and contribute to improved patient care.