Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

TLDR

Wireless body sensor networks promise patient‑centric telecardiology, yet current ECG monitors fall short of the required miniaturization and energy efficiency. The study quantifies the potential of compressed sensing for low‑complexity, energy‑efficient ECG compression on a Shimmer WBSN mote. The authors evaluate compressed sensing as an embedded ECG compression technique to reduce wireless airtime and improve energy efficiency on the Shimmer mote. Compressed sensing achieves comparable reconstruction quality to DWT but with lower complexity, yielding a 37.1 % longer node lifetime compared to DWT‑based compression.

Abstract

Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for “good” reconstruction quality.