Classification of epileptic EEG signals using sparse spectrum based empirical wavelet transform

The unnatural activities of brain due to seizure events are analysed by electroencephalogram (EEG) signals which are captured from the brain. In this work, a methodology is proposed to classify the seizure EEG signals. In the proposed method, a novel sparse spectrum based empirical wavelet transform...

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Veröffentlicht in:	Electronics letters Jg. 56; H. 25; S. 1370 - 1372
Hauptverfasser:	Nishad, A, Upadhyay, A, Ravi Shankar Reddy, G, Bajaj, V
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 10.12.2020
Schlagworte:	brain activity classification accuracy classification problem cross‐information potential electroencephalogram signals electroencephalography epileptic EEG signals feature extraction k‐nearest neighbour classifier medical disorders medical signal processing nearest neighbour methods normalised energy RELIEFF method seizure EEG signals seizure events seizure‐free EEG signals signal classification sparse spectrum based empirical wavelet transform Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications SS‐EWT coefficients unnatural activities wavelet transforms Z EEG signals seizure-free EEG signals electroencephalography Z EEG signals wavelet transforms electroencephalogram signals seizure events cross-information potential classification accuracy k-nearest neighbour classifier nearest neighbour methods signal classification normalised energy medical disorders classification problem medical signal processing epileptic EEG signals feature extraction seizure EEG signals unnatural activities sparse spectrum based empirical wavelet transform RELIEFF method brain activity SS-EWT coefficients
ISSN:	0013-5194, 1350-911X, 1350-911X
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Zusammenfassung:	The unnatural activities of brain due to seizure events are analysed by electroencephalogram (EEG) signals which are captured from the brain. In this work, a methodology is proposed to classify the seizure EEG signals. In the proposed method, a novel sparse spectrum based empirical wavelet transform (SS-EWT) is applied to decompose the EEG signal into coefficients. From the obtained SS-EWT coefficients, the cross-information potential and normalised energy are extracted as features. Then these features are ranked using the RELIEFF method to obtain significant features. After ranking, these features are fed into the k-nearest neighbour (k-NN) classifier to classify EEG signals corresponding to different brain activities. In this work, the first classification problem is the classification of the seizure (S), seizure-free (F), and normal (Z) EEG signals in which obtained classification accuracy (Acc) is $96.67\%$96.67%. The second classification problem is the classification of S and Z EEG signals in which $100\%$100% Acc is achieved by the proposed method.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2020.2526