An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

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.

Towards Energy Efficiency in the Internet of Wearable Things: A Systematic Review

“…Content may change prior to final publication. Embedded signal processing to avoid unnecessary data transmission by carrying out local processing [54], [57], [58], [87] Compressive sensing to avoid generating unnecessary redundant data [53], [84], [92], [99] Embedded ML to carry out complex computation tasks on the device to reduce latency Wireless technologyrelated issues A, HW [45] Duty cycling to periodically power off communication modules when not in use to conserve energy [49], [61] Adaptive transmission power control of transceiver based on distance to remote node [59] Use of low power wireless communication protcols such as 6LoWPAN [61] A multi channel TDMA approach to allocate slots for simultaneous transmission [109] An adaptive connection interval selection in dynamic channel environments for improved connectivity [98], [148] Low power wireless communication protocols…”

Energy‐Saving Multisensor Data Sampling and Fusion with Decision‐Making for Monitoring Health Risk Using WBSNs

“…It is mainly determined by signal sparsity/compressibility, as the bio-signals like the electrocardiogram (ECG) are sparse. [22][23][24] In order to reduce the power transmission consumption, the researchers in Reference 23 have suggested the block sparse Bayesian learning (BSBL) for bioelectric signals with sparse binary matrixes. In Reference 24, the researchers indicate that energy efficiency can be achieved through the use of hardware friendly structured sensing matrices such as CS-based multichannel EEG monitoring.…”

Compressive Sensing-Based IoT Applications: A Review

“…CS is expected to optimize power and energy used in wireless ambulatory devices, hence extending the sensor lifespan and significantly simplifying hardware design and reducing both the size and cost of the entire healthcare platform. CS-based healthcare applications include medical imaging [9], electrocardiogram (ECG) monitoring [10], EEG compression [11], biometric solutions [12], etc.…”