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Sensors 2019, 19(2), 219; https://doi.org/10.3390/s19020219

Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer

1
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
2
Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 17 November 2018 / Revised: 23 December 2018 / Accepted: 3 January 2019 / Published: 9 January 2019
(This article belongs to the Special Issue Sensors for Biosignal Processing)
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Abstract

Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data. View Full-Text
Keywords: recognition of epilepsy EEG; Symlet wavelet; gradient boosting machine; grid search optimizer; multiple performance indices evaluation recognition of epilepsy EEG; Symlet wavelet; gradient boosting machine; grid search optimizer; multiple performance indices evaluation
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Wang, X.; Gong, G.; Li, N. Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer. Sensors 2019, 19, 219.

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