Abstract
Electroencephalography (EEG) signal is vital for the detection and analysis of brain-related disorders. Brain waves are non-sparse in either in time or many other transform domains. In this paper, the proposed algorithm applies optimal mother wavelet for each block rather than applying a single mother wavelet for sparsification of the entire EEG signal. Sparsification of individual blocks is done based on the least percentage difference metric in the work. Block adaptive decomposition has been performed for implementation of compressive sensing of single-channel EEG signal and the proposed method has been tested over different datasets. Results reveal that the average Percentage Root mean square Difference (PRD) value of 0.14 and 0.06 achieved are very close to 0% and fits into the “excellent” reconstruction quality class of 0–2%. This demonstrates that the proposed algorithm has higher fidelity and is very apt for biomedical analysis for the detection of epileptic disorder using the recovered signal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang J, Yu L Li Y (2015) Energy-efficient ECG compression on wireless biosensors via minimal coherence sensing and weighted l1 minimization reconstruction. IEEE J Biomed Health Inf 19(2)
Mihajlovi V, Grundlehner B, Vullers R, Penders J (2015) Wearable,wireless EEG solutions in daily life applications: what are we missing? IEEE J Biomed Health Inf 19(1):6–21
Mamaghanian H, Khaled N, Atienza D (2011) Compressed sensing for real-time energy-efficient ECG compression on wbsn. IEEE Trans Biomed Eng 58(9)
Casson AJ et al. (2010) Wearable electroencephalography. IEEE Eng Med Bıol Mag
Mishra I, Jain S (2021) Soft computing based compressive sensing techniques in signal processing: a comprehensive review. J Intell Syst 30:312–326. De Gruyter
Mousavi S, Afghah F, Acharya UR (2019) SleepEEGNet automated sleep stage scoring with sequence to sequence deep learning approach. PloS ONE 14(5)
Dufort G, Favaro F, Lecumberry F (2016) Wearable EEG via lossless compression. IEEE
Zhang Z, Jung TP, Makeig S, Pi Z, Rao BD (2014) Applications to compressed sensing of multichannel physiological signals. IEEE Trans Neural Syst Rehabıl Eng
Ravelomanantsoa A, Rabah H, Rouane A (2014) Simple and efficient compressed sensing encoder for wireless body area network. IEEE Trans Instrum Meas 63
CHBMIT Scalp EEG database. https://archive.physionet.org/physiobank/database/chbmit/
Pei Z, Wang Y (2017) Energy efficient compressed sensing of bio-signals with sparse binary matrix. In: 4th International conference on ınformation science and control engineering (ICISCE)
Zhang Z, Jung TP, Makeig S, Rao BD (2013) Compressed Sensing of EEG for wireless telemonitoring with low energy consumption and ınexpensive hardware. IEEE Trans Bıomed Eng 60(1)
Singh W, Shukla A, Debn S, Majumdar A (2017) Energy efficient EEG acquisition and reconstruction for a wireless body area network. Integratıon 295–302. Elsevier
Shukla A, Majumdarm A (2015) Exploiting inter-channel correlation in EEG signal reconstruction. Biomed Signal Process Control 49–55. Elsevier
Brechet L, Lucas MF, Doncarli C, Farina D (2007) Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection. IEEE Trans Biomed Eng 54 (12)
Motinath VA, Jha CK, Kolekar MH (2016) A novel EEG data compression algorithm using best mother wavelet selection. In: IEEE International conference on advances in computing, communications and ınformatics (ICACCI). Jaipur, India
Candès EJ, Wakin MB (2008) An ıntroduction to compressive sampling. IEEE Signal Process Mag 21–30
Dey MR Shiraz A, Sharif S, Lota J (2020) Dictionary selection for compressed sensing of EEG signals using sparse binary matrix and spatiotemporal sparse Bayesian learning. Biomed Phys Eng Expres. IOP Publishing Ltd
MIT-BIH arrhythmia database. www.physionet.org/mitdb/-
Matran-Fernandez A, Polo R (2017) Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces. PloS ONE 12(5)
Berg E, Friedlander MP (2015) SPGL1: a solver for large-scale sparse reconstruction. http://www.cs.ubc.ca/labs/scl/spgl1
Gurve D, Delisle-Rodriguez D, Bastos-Filho T, Krishnan S (2020) Trends in compressive sensing for EEG signal processing applications. Sensors
Wu D, Yang B, Wang H, Wu D, Wang R (2016) An energy-efficient data forwarding strategy for heterogeneous WBANs. IEEE Access 4
Acknowledgements
The authors would like to thank all the anonymous reviewers for their critical analysis and constructive suggestions which enhanced the quality of the paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kunabeva, R., Vinutha, L.B., Manjunatha, P. (2022). In-Node Adaptive Compressive Sensing Technique for EEG Signal in WBAN. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_54
Download citation
DOI: https://doi.org/10.1007/978-981-16-6460-1_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6459-5
Online ISBN: 978-981-16-6460-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)