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Research Articles

Evaluation of time domain features using best feature subsets based on mutual information for detecting epilepsy

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Pages 487-500 | Received 13 Dec 2017, Accepted 13 Jan 2019, Published online: 15 Mar 2019
 

Abstract

In this pattern recognition study of detecting epilepsy, the first time the authors have attempted to use time domain (TD) features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) which are extracted from the discrete wavelet transform (DWT) for the detecting the epilepsy for University of Bonn datasets and real-time clinical data. The performance of these TD features is studied along with mean absolute value (MAV) which has been attempted by other researchers. The performance of the TD features derived from DWT is studied using naive Bayes (NB) and support vector machines (SVM) for five different datasets from University of Bonn with 14 different combinations datasets and 24 patients datasets from Christian Medical College and Hospital (CMCH), India database. Using feature selection and feature ranking based on the estimation of mutual information (MI), the significant features required for the classifier to get higher accuracy is obtained. Further, NB achieves 100% classification accuracy (CA) in distinguishing normal eyes open and epileptic dataset with top 4 ranked features and it gives 100% accuracy with top-ranked two features in using CMCH data.

Acknowledgements

We thank Dr. Vivek Mathew, Professor, Department of Neuroscience, Christian Medical College and Hospital, India, for his valuable support to provide clinical real-time dataset.

Disclosure statement

We declare that we have no conflict of interest.

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