A Review on Intelligent Systems for ECG Analysis: From Flexible Sensing Technology to Machine Learning

Abstract

This paper conducts an extensive review of flexible cardiac sensing devices designed for electrocardiogram (ECG) acquisitions, with emphasis on their application in cardiac health monitoring. This study focuses on characteristics crucial to these devices, including: flexibility, durability, biocompatibility, sensitivity, and stretchability. It provides a comprehensive overview of prevalent fabrication methods and materials employed for flexible electrode production, with insights from several studies that utilize these electrodes across diverse applications. Furthermore, the review highlights the significant role of machine learning (ML) in cardiac health monitoring and broader ECG analysis applications. With the most used methods being deep learning, support vector machines, random forest, and linear discriminant analysis, the paper delves into studies that leverage ML for heart disease classification as well as other applications such as emotion detection and biometric recognition. The paper culminates with an overview of studies that integrate both flexible sensing technology and ML, particularly in the domain of cardiac health monitoring. It sheds light on the important relationship between these two techniques, underscoring their impact on advancing ECG-based health monitoring methodologies. Besides reviewing the current state of these technologies, the paper also outlines future perspectives and potential directions for research in this domain.