Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epilepti...

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Main Author:	A. Abualsaud, Khalid Ahmed
Format:	Thesis
Language:	eng eng eng
Published:	2015
Subjects:	TK7885-7895 Computer engineering Computer hardware
Online Access:	https://etd.uum.edu.my/5761/1/depositpermission_s93034.pdf https://etd.uum.edu.my/5761/2/s93034_01.pdf https://etd.uum.edu.my/5761/3/s93034_02.pdf
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id	my-uum-etd.5761
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Mahmuddin, Massudi
topic	TK7885-7895 Computer engineering Computer hardware
spellingShingle	TK7885-7895 Computer engineering Computer hardware A. Abualsaud, Khalid Ahmed Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
description	The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	A. Abualsaud, Khalid Ahmed
author_facet	A. Abualsaud, Khalid Ahmed
author_sort	A. Abualsaud, Khalid Ahmed
title	Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
title_short	Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
title_full	Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
title_fullStr	Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
title_full_unstemmed	Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
title_sort	ensemble approach on enhanced compressed noise eeg data signal in wireless body area sensor network
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/5761/1/depositpermission_s93034.pdf https://etd.uum.edu.my/5761/2/s93034_01.pdf https://etd.uum.edu.my/5761/3/s93034_02.pdf
_version_	1813495779898163200
spelling	my-uum-etd.57612024-09-19T15:49:29Z Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network 2015 A. Abualsaud, Khalid Ahmed Mahmuddin, Massudi Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences TK7885-7895 Computer engineering. Computer hardware The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems. 2015 Thesis https://etd.uum.edu.my/5761/ https://etd.uum.edu.my/5761/1/depositpermission_s93034.pdf text eng staffonly https://etd.uum.edu.my/5761/2/s93034_01.pdf text eng public https://etd.uum.edu.my/5761/3/s93034_02.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia [1] M. Hanson, “Wireless Body Area Sensor Network Technology for Motion-Based Health Assessment”, A Dissertation presented to University of Virginia, for the Degree Doctor of Philosophy, USA, August 2009. [2] I. Stojmenovi, “Handbook of Sensor Networks: Algorithms and Architectures”, University of Ottawa, John Wiley & Sons, Inc., 2005. [3] J. Lach, J. Aylor, N. Merris, M. Hanson, C. Rehorn, "Wearable Gait Data Collection for Longitudinal Fall Analysis", International Conference on Aging, Disability and Independence, Virginia, USA, 2003. [4] D. 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