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AuthorDjelouat, Hamza
AuthorBaali, Hamza
AuthorAmira, Abbes
AuthorBensaali, Faycal
Available date2022-12-29T07:34:44Z
Publication Date2017
Publication NameWireless Communications and Mobile Computing
ResourceScopus
URIhttp://dx.doi.org/10.1155/2017/9823684
URIhttp://hdl.handle.net/10576/37834
AbstractThe last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG) sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP) algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process. 2017 Hamza Djelouat et al.
SponsorThis paper was made possible by National Priorities Research Program (NPRP) grant from the Qatar National Research Fund (a member of Qatar Foundation) (Grant no. 9-114-2-055).
Languageen
PublisherHindawi Limited
SubjectBiomedical signal processing
Brain
Complex networks
Diagnosis
Electroencephalography
Energy efficiency
Internet of things
Neurophysiology
Wearable sensors
Compression techniques
Compressive sensing
Electro-encephalogram (EEG)
Health care application
High power consumption
Internet of thing (IOT)
Long term monitoring
Reconstruction quality
Compressed sensing
TitleAn Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
TypeArticle
Volume Number2017


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