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AuthorHussein R.
AuthorMohamed A.
AuthorAlghoniemy M.
Available date2022-04-21T08:58:30Z
Publication Date2015
Publication NameBiomedical Signal Processing and Control
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.bspc.2015.03.005
URIhttp://hdl.handle.net/10576/30134
AbstractRecent technological advances in wireless body sensor networks have made it possible for the development of innovative medical applications to improve health care and the quality of life. By using miniaturized wireless electroencephalography (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time healthcare applications. One master consideration in using such battery-powered wireless EEG monitoring system is energy constraint at the sensor side. The traditional EEG streaming approach imposes an excessive power consumption, as it transmits the entire EEG signals wirelessly. Therefore, innovative solutions to alleviate the total power consumption at the receiver are highly desired. This work introduces the use of the discrete wavelet transform and compressive sensing algorithms for scalable EEG data compression in wireless sensors in order to address the power and distortion constraints. Encoding and transmission power models of both systems are presented which enable analysis of power and performance costs. We then present a theoretical analysis of the obtained distortion caused by source encoding and channel errors. Based on this analysis, we develop an optimization scheme that minimizes the total distortion for different channel conditions and encoder settings. Using the developed framework, the encoder can adaptively tune the encoding parameters to match the energy constraint without performance degradation. 2015 Elsevier Ltd. All rights reserved.
SponsorChinese Academy of Agricultural Sciences;National Key Research and Development Program of China
Languageen
PublisherElsevier Ltd
SubjectChannel coding
Compaction
Convex optimization
Discrete wavelet transforms
Electric power utilization
Electroencephalography
Electrophysiology
Encoding (symbols)
Energy efficiency
Health care
Medical applications
Signal encoding
Encoding parameters
Innovative solutions
Performance degradation
Real-time healthcares
Technological advances
Total power consumption
Wireless body area sensor network
Wireless body sensor networks
Body sensor networks
accuracy
algorithm
Article
digital compression
electroencephalogram
electroencephalography
electronic sensor
energy
energy consumption
human
information processing
performance
priority journal
process optimization
seizure
wavelet analysis
wireless body area sensor network
wireless communication
TitleScalable real-time energy-efficient EEG compression scheme for wireless body area sensor network
TypeArticle
Pagination122-129
Volume Number19
dc.accessType Abstract Only


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