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Sensors 2014, 14(1), 1474-1496; doi:10.3390/s140101474

An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals

Department of Electrical and Computer Engineering, The University of British Columbia, 2322 Main Mall, Vancouver, BC V6T1Z4, Canada
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Received: 14 December 2013 / Revised: 8 January 2014 / Accepted: 10 January 2014 / Published: 15 January 2014
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
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Abstract

The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person’s health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.
Keywords: compressed sensing (CS); electroencephalography (EEG); wireless body sensor network (WBSN); telemedicine; biomedical signal processing compressed sensing (CS); electroencephalography (EEG); wireless body sensor network (WBSN); telemedicine; biomedical signal processing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Fauvel, S.; Ward, R.K. An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals. Sensors 2014, 14, 1474-1496.

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