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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(4): 288-293 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.522

Wavelet Transform Enhancement for Drowsiness Classification in EEG Records Using Energy Coefficient Distribution and Neural Network

Naiyana Boonnak, Suwatchai Kamonsantiroj, and Luepol Pipanmaekaporn
Abstract—Reliable classification of drowsy stage in EEG signals have attracted attentions from researchers for many years because of large amounts of brain signal noise. Recent studies have demonstrated that the analysis of EEG signals can get benefits from wavelet transform (WT). Despite of this, experiments do not support the effective use of wavelet features for the discrimination of EEG signals because there is much redundant and irrelevant information contained in wavelet coefficients. Furthermore, extraction of useful features from EEG signals for classification is still an open research question. The novel method present in this paper is to extract useful features for classification of EEG signals based on wavelet transform. This method basically consists of two major steps. The first step is extracting energy coefficients from wavelet transform based on Parseval’s theorem to represent the distribution of brain signals. The second step focuses on revising weights of energy coefficients to facilitate a classification method. We show that the energy-based features not only capture meaningful information of wavelet transform, but also are useful for classification. We evaluate the proposed method by using the energy-based features to train a neural network for classification of drowsy and alert signals in EEG records. The experimental results conducted on the MIT-BIH Polysomnographic database have shown that the proposed method achieves 90.27% of accuracy compared to wavelet-based methods.

Index Terms—EEG, drowsiness, alertness, wavelet transform, energy distribution, neural network, classification.

The authors are with the Department of Computer and Information Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand 10800 (e-mail: getaaun9@gmail.com, suwatchaik@kmutnb.ac.th, luepolp@kmutnb.ac.th).

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Cite: Naiyana Boonnak, Suwatchai Kamonsantiroj, and Luepol Pipanmaekaporn, "Wavelet Transform Enhancement for Drowsiness Classification in EEG Records Using Energy Coefficient Distribution and Neural Network," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 288-293, 2015.

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