An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
Author | Djelouat, Hamza |
Author | Baali, Hamza |
Author | Amira, Abbes |
Author | Bensaali, Faycal |
Available date | 2022-12-29T07:34:44Z |
Publication Date | 2017 |
Publication Name | Wireless Communications and Mobile Computing |
Resource | Scopus |
Abstract | The 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. |
Sponsor | This 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). |
Language | en |
Publisher | Hindawi Limited |
Subject | Biomedical 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 |
Type | Article |
Volume Number | 2017 |
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