Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware

TL;DR

This paper explores the use of compressed sensing with Block Sparse Bayesian Learning to efficiently telemonitor EEG signals wirelessly, reducing energy consumption and hardware costs while maintaining clinical quality.

Contribution

It introduces BSBL to EEG telemonitoring, demonstrating superior recovery quality over existing CS algorithms for non-sparse signals.

Findings

BSBL outperforms state-of-the-art CS algorithms in EEG recovery quality.

The method is suitable for practical telemonitoring applications.

Results indicate potential for low-energy, low-cost wireless EEG systems.

Abstract

Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is non-sparse in the time domain and also non-sparse in transformed domains (such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, Block Sparse Bayesian Learning (BSBL) was proposed as a new method to the CS problem. This study introduces the technique to the telemonitoring of EEG. Experimental results show that its recovery quality is better than state-of-the-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very promising for telemonitoring of EEG and other non-sparse physiological signals.