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An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals
Sensors 2014, 14(2), 2036-2051; doi:10.3390/s140202036

Energy-Efficient Data Reduction Techniques for Wireless Seizure Detection Systems

*  and
Department of Electrical and Computer Engineering, The University of British Columbia, 2332 Main Mall, Vancouver, BC V6T 1Z4, Canada
* Author to whom correspondence should be addressed.
Received: 2 December 2013 / Revised: 31 December 2013 / Accepted: 17 January 2014 / Published: 24 January 2014
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
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The emergence of wireless sensor networks (WSNs) has motivated a paradigm shift in patient monitoring and disease control. Epilepsy management is one of the areas that could especially benefit from the use of WSN. By using miniaturized wireless electroencephalogram (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time seizure detection outside clinical settings. One major consideration in using such a wireless EEG-based system is the stringent battery energy constraint at the sensor side. Different solutions to reduce the power consumption at this side are therefore highly desired. The conventional approach incurs a high power consumption, as it transmits the entire EEG signals wirelessly to an external data server (where seizure detection is carried out). This paper examines the use of data reduction techniques for reducing the amount of data that has to be transmitted and, thereby, reducing the required power consumption at the sensor side. Two data reduction approaches are examined: compressive sensing-based EEG compression and low-complexity feature extraction. Their performance is evaluated in terms of seizure detection effectiveness and power consumption. Experimental results show that by performing low-complexity feature extraction at the sensor side and transmitting only the features that are pertinent to seizure detection to the server, a considerable overall saving in power is achieved. The battery life of the system is increased by 14 times, while the same seizure detection rate as the conventional approach (95%) is maintained.
Keywords: electroencephalography; wireless sensor networks; seizure detection; compressive sensing; feature extraction electroencephalography; wireless sensor networks; seizure detection; compressive sensing; feature extraction
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Chiang, J.; Ward, R.K. Energy-Efficient Data Reduction Techniques for Wireless Seizure Detection Systems. Sensors 2014, 14, 2036-2051.

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