REAL-TIME PROCESSING STAGES OF ELECTROCARDIOGRAM SIGNAL: A REVIEW

Abstract

Signal analysis is a multidisciplinary field that combines various processes to create robust pipelines for automating data analysis. Within the medical field, its application revolves around physiological signals. Electrocardiogram (ECG) signal provides vital information concerning various cardiac conditions affecting the human heart. ECG analysis is a central pillar of medical research with the goal of detecting and preventing potentially fatal cardiac events. This review article aims to provide a comprehensive analysis of real-time processing techniques for electrocardiogram signals. It discusses the different methods and algorithms used for detecting and analyzing ECG signals in real-time, with a focus on their effectiveness and efficiency. Where, it evaluates the use of various techniques such as ECG signal preprocessing (denoising), ECG fiducial points detecting and ECG signal classification. It also highlights the challenges faced in real-time ECG signal processing, such as High-fidelity signal acquisition and noise reduction, and computational efficiency and resource constraints. Furthermore, the article presents an overview of the existing QRS-peak detection strategies, including methods such as Haar wavelet transform, and modified MaMeMi Filter.