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

• Published in: IEEE transactions on biomedical engineering Vol. 60; no. 1; pp. 221 - 224
• Main Authors: Zhang, Zhilin, Jung, Tzyy-Ping, Makeig, Scott, Rao, Bhaskar D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2013
• Subjects: Algorithms; Bayes Theorem; Block sparse Bayesian learning (BSBL); Compressed sensing; compressed sensing (CS); Databases, Factual; Dictionaries; electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Energy consumption; healthcare; Humans; Remote Sensing Technology - methods; Sensors; Signal Processing, Computer-Assisted; Sparse matrices; Telemedicine - methods; telemonitoring; Wavelet transforms; wireless body-area network (WBAN); Wireless Technology
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2012.2217959

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 nonsparse in the time domain and also nonsparse 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 nonsparse physiological signals.