An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

المؤلفون المشاركون

Djelouat, Hamza
Baali, Hamza
Amira, Abbes
Bensaali, Faycal

المصدر

Wireless Communications and Mobile Computing

العدد

المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-10، 10ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2017-11-29

دولة النشر

مصر

عدد الصفحات

10

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الملخص EN

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.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Djelouat, Hamza& Baali, Hamza& Amira, Abbes& Bensaali, Faycal. 2017. An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System. Wireless Communications and Mobile Computing،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1206388

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Djelouat, Hamza…[et al.]. An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System. Wireless Communications and Mobile Computing No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1206388

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Djelouat, Hamza& Baali, Hamza& Amira, Abbes& Bensaali, Faycal. An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System. Wireless Communications and Mobile Computing. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1206388

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1206388