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Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

Lai, Kee Huong and Zainuddin, Zarita and Ong, Pauline (2013) Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection. In: Proceedings of the 21st National Symposium on Mathematical Sciences (SKSM21), 6-8 November 2013, Penang, Malaysia.

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Abstract

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: feature selection; principal component analysis; harmony search; wavelet neural networks; seizure detection
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Engineering Mechanics
Depositing User: Normajihan Abd. Rahman
Date Deposited: 29 Mar 2015 09:00
Last Modified: 29 Mar 2015 09:00
URI: http://eprints.uthm.edu.my/id/eprint/6510
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