Effectiveness of combined time-frequency imageand signal-based features for improving the detection and classification of epileptic seizure activities in EEG signals

Boubchir, Larbi, Al-Maadeed, Somaya and Bouridane, Ahmed (2014) Effectiveness of combined time-frequency imageand signal-based features for improving the detection and classification of epileptic seizure activities in EEG signals. In: Codit'14 - 2nd International Conference on Control, Decision and Information Technologies, 3rd - 5th November 2014, Metz, France.

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Official URL: http://dx.doi.org/10.1109/CoDIT.2014.6996977

Abstract

This paper presents new time-frequency (T-F) features to improve the detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals generated from different spectral sub-bands. The proposed features are based on T-F image descriptors, which are extracted from the T-F representation of EEG signals, are considered and processed as an image using image processing techniques. The idea of the proposed feature extraction method is based on the application of Otsu's thresholding algorithm on the T-F image in order to detect the regions of interest where the epileptic seizure activity appears. The proposed T-F image related-features are then defined to describe the statistical and geometrical characteristics of the detected regions. The results obtained on real EEG data suggest that the use of T-F image based-features with signal related-features improve significantly the performance of the EEG seizure detection and classification by up to 5% for 120 EEG signals, using a multi-class SVM classifier.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Feature extraction; Image processing; Neurodegenerative diseases; Neurophysiology; Signal detection
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Physics and Electrical Engineering
Depositing User: Paul Burns
Date Deposited: 06 Feb 2015 15:09
Last Modified: 10 Aug 2015 11:03
URI: http://nrl.northumbria.ac.uk/id/eprint/21334

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