A Review of Deep Learning Approaches Using ECG Signal Analysis

Abstract

Electrocardiograms (ECGs) are non-stationary signals that are often used to assess heartbeat tuning and rate. In order to monitor cardiovascular health and identify diseases, electrocardiogram (ECG) signals are an essential diagnostic tool. This study offers a thorough examination of the core ideas, preprocessing strategies, and deep learning approaches used in ECG analysis. It begins by detailing the structure and components of ECG signals, common acquisition methods, and the major noise sources that affect signal quality. Preprocessing stages, including denoising, segmentation, feature extraction, and feature selection, are discussed with an emphasis on their role in improving classification accuracy. The paper also examines cutting-edge deep learning architectures that have demonstrated significant promise in automating ECG-based diagnoses, including Transformer models, Autoencoders, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid CNN-RNN models. Practical applications such as arrhythmia detection, rhythm classification, sleep and stress monitoring, noise removal, and real-time analysis in wearable health devices are reviewed. The integration of such models into wearable technologies, exemplified by devices like ECG WATCH, indicates a significant shift towards real-time, personalized cardiac care.