Classification of epileptic EEG signals using sparse spectrum based empirical wavelet transform. Issue 25 (8th October 2020)

• Record Type: Journal Article
• Authors: Nishad, A.; Upadhyay, A.; Ravi Shankar Reddy, G.; Bajaj, V.
• Is Part Of: Electronics letters. Volume 56:Issue 25(2020)
• Page Start: 1370
• Page End: 1372
• Publication Date: 2020-10-08
• Subjects: wavelet transforms -- signal classification -- electroencephalography -- medical signal processing -- nearest neighbour methods -- feature extraction -- medical disorders; epileptic EEG signals -- unnatural activities -- seizure events -- electroencephalogram signals -- seizure EEG signals -- SS‐EWT coefficients -- cross‐information potential -- normalised energy -- RELIEFF method -- k‐nearest neighbour classifier -- brain activity -- classification problem -- classification accuracy -- Z EEG signals -- sparse spectrum based empirical wavelet transform -- feature extraction -- seizure‐free EEG signals; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.2526
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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 % . The second classification problem is the classification of S and Z EEG signals in which 100 % Acc is achieved by the proposed method.