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

Content Provider	IET Digital Library
Author	Nishad, A. Upadhyay, A. Reddy, G. Ravi Shankar Bajaj, Varun
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.
Starting Page	1370
Ending Page	1372
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.2526
Journal	Electronics Letters
Publisher Date	2020-10-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bioelectric Signal Biology And Medical Computing Brain Activity Classification Accuracy Classification Problem Cross-information Potential Digital Signal Processing Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalogram Signal Electroencephalography Epileptic EEG Signal Feature Extraction Function Theory And Analysis Integral Transforms K-nearest Neighbour Classifier Knowledge Engineering Technique Medical Disorders Medical Signal Processing Nearest Neighbour Method Normalised Energy RELIEFF Method Seizure EEG Signal Seizure Events Seizure-free EEG Signal Signal Classification Signal Processing And Detection Sparse Spectrum Based Empirical Wavelet Transform SS-EWT Coefficient Unnatural Activities Wavelet Transform Z EEG Signal
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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