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Open AccessArticle

Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography

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Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
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Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh 2224, Bangladesh
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Institute of Global Innovation Research, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
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Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
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RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
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Authors to whom correspondence should be addressed.
This paper is an extended version of our paper published in “Kazi Mahmudul Hassan, Md. Rabiul Islam, Toshihisa Tanaka and Md. Khademul Islam Molla; Epileptic Seizure Detection from EEG Signals Using Multiband Features with Feedforward Neural Network” 2019 International Conference on Cyberworlds (CW).
Sensors 2020, 20(16), 4639; https://doi.org/10.3390/s20164639
Received: 7 June 2020 / Revised: 14 August 2020 / Accepted: 15 August 2020 / Published: 18 August 2020
(This article belongs to the Section Biomedical Sensors)
Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into short time frames. Discrete wavelet transform is used to decompose each frame into a number of subbands. Different entropies as well as a group of features with which to characterize the spike events are extracted from each subband signal of an EEG frame. The features extracted from individual subbands are concatenated, yielding a high-dimensional feature vector. A discriminative subset of features is selected from the feature vector using a graph eigen decomposition (GED)-based approach. Thus, the reduced number of features obtained is effective for differentiating the underlying characteristics of EEG signals that indicate seizure events and those that indicate nonseizure events. The GED method ranks the features according to their contribution to correct classification. The selected features are used to classify seizure and nonseizure EEG signals using a feedforward neural network (FfNN). The performance of the proposed method is evaluated by conducting various experiments with a standard dataset obtained from the University of Bonn. The experimental results show that the proposed seizure-detection scheme achieves a classification accuracy of 99.55%, which is higher than that of state-of-the-art methods. The efficiency of FfNN is compared with linear discriminant analysis and support vector machine classifiers, which have classification accuracies of 98.72% and 99.39%, respectively. Hence, the proposed method is confirmed as a potential marker for EEG-based seizure detection. View Full-Text
Keywords: discrete wavelet transform; electroencephalography; feature selection; epilepsy; seizure discrete wavelet transform; electroencephalography; feature selection; epilepsy; seizure
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MDPI and ACS Style

Molla, M.K.I.; Hassan, K.M.; Islam, M.R.; Tanaka, T. Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography. Sensors 2020, 20, 4639.

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