Abualsaud, Khalid and Mahmuddin, Massudi and Hussein, Ramy and Mohamed, Amr (2013) Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring. In: 9th International Wireless Communications and Mobile Computing Conference (IWCMC 2013), 1-5 July 2013, Sardinia, Italy.

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Abstract

Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities.EEG signals capture important information pertinent to different physiological brain states.In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring.The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression.To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie’s wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification.Extensive experimental work is conducted, utilizing four classification models.The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value.The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application’s accuracy.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Print ISBN: 978-1-4673-2479-3
Uncontrolled Keywords:	EEG; DCT; wavelet compression; compressive sensing; feature extraction; classification.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	College of Arts and Sciences
Depositing User:	Dr. Massudi Mahmuddin
Date Deposited:	15 Nov 2015 08:41
Last Modified:	15 Nov 2015 08:41
URI:	https://repo.uum.edu.my/id/eprint/16160

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