Detection of epileptic seizure and seizure-free EEG signals employing generalised S -transform
- Author(s): Soumya Chatterjee 1 ; Niladri Ray Choudhury 1 ; Rohit Bose 2
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View affiliations
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Affiliations:
1:
Electrical Engineering Department , Jadavpur University , Kolkata , India ;
2: Electrical Engineering Department , Calcutta Institute of Engineering and Management , Kolkata , India
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Affiliations:
1:
Electrical Engineering Department , Jadavpur University , Kolkata , India ;
- Source:
Volume 11, Issue 7,
October
2017,
p.
847 – 855
DOI: 10.1049/iet-smt.2016.0443 , Print ISSN 1751-8822, Online ISSN 1751-8830
In this contribution, a novel technique for classification of electroencephalogram (EEG) signals has been presented employing generalised Stockwell ( S )-transform technique. S -transform is a technique for analysis of any non-stationary time series in joint time–frequency frame. In this work, epileptic seizure and seizure-free EEG signals have been taken from an available existing database and generalised S -transform is applied individually on different sets of EEG signals. Selective features like standard deviation and energy are evaluated from the joint time–frequency S -transform contour of the transformed signals and are eventually being classified using support vector machines (SVMs) and k-nearest neighbour (kNN) classifier. In this work, two different classification problems are addressed, namely (i) seizure and healthy (ii) seizure and inter-ictal, where both EEG signals of healthy and inter-ictal zone are considered to be in seizure-free class. For different cases investigated in this study, the highest overall classification accuracy of 98.44% is achieved using SVM classifier where as 100% accuracy is obtained using kNN classifier, which are comparable and even better than the results obtained in the existing literatures, analysed on the same dataset.
Inspec keywords: support vector machines; electroencephalography; transforms
Other keywords: epileptic seizure detection; support vector machines; generalised S-transform; seizure-free EEG signals; joint time–frequency S-transform contour; electroencephalogram signals; k-nearest neighbour classifier
Subjects: Bioelectric signals; Function theory, analysis; Integral transforms in numerical analysis; Electrodiagnostics and other electrical measurement techniques
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