Opportunities and Challenges in Deep Compressed Sensing Techniques for Multichannel ECG Data Compression

Energy consumption involved in wireless transmission poses a major challenge in the implementation of wireless body area networks (WBAN). Compressed sensing (CS)-based multichannel electrocardiogram (ECG) compression is a new paradigm in signal acquisition and reconstruction; a viable alternative for traditional wavelet-based signal reconstruction. However, several challenges must be addressed to achieve efficient and reliable ECG compression in real-world wireless healthcare systems. This paper presents a comprehensive review focused on the evolution of compressed sensing-based energy-efficient single-channel (S-) and multi-channel (M-) ECG data compression techniques. It is observed that the performance of different compression techniques depends on several diagnostic or non-diagnostic test parameters. The present study could be useful for researchers to analyze the state-of-the-art compression techniques in e-healthcare applications. We discuss the challenges associated with implementing ECG compression in wireless healthcare systems, such as signal quality, interoperability, and privacy concerns. We also explore the potential future directions for research in this area, including the development of novel algorithms for compressed sensing-based ECG compression, the integration of artificial intelligence and deep learning techniques, and the exploration of new application areas for wireless healthcare systems. This paper will serve as a good reference for the researcher interested in the area of wireless transmission for WBAN applications.