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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Bibliographic Details
Published in:Electronics letters Vol. 56; no. 25; pp. 1370 - 1372
Main Authors: Nishad, A, Upadhyay, A, Ravi Shankar Reddy, G, Bajaj, V
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 10.12.2020
Subjects:
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
Online Access:Get full text
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Summary: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