Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier

• Published in: Journal of medical systems Vol. 41; no. 10; p. 160
• Main Authors: Sriraam, N., Raghu, S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-10-2017; Springer Nature B.V
• Subjects: Accuracy; Bayes Theorem; Bayesian analysis; Classification; Classifiers; Convulsions & seizures; EEG; Electroencephalography; Epilepsy; Feature extraction; Feature recognition; Health Informatics; Health Sciences; Humans; Image & Signal Processing; Medicine; Medicine & Public Health; Optimization; Outliers (statistics); Patients; Performance measurement; Pharmacology; Pruning; Seizures; Simulation; Statistics for Life Sciences; Support Vector Machine; Support vector machines; Surgery; Turkey; Epileptic seizures; Classifier; SVM; Focal and non focal; EEG; Multi-feature
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-017-0800-x

Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed. Total 26 features were extracted from focal and non focal EEG. Significant features were selected using Wilcoxon rank sum test by setting
p
-value (
p
< 0.05) and
z
-score (−1.96 >
z
> 1.96) at 95% significance interval. Hypothesis was made that the effect of removing outliers improves the classification accuracy. Turkey’s range test was adopted for pruning outliers from feature set. Finally, 21 features were classified using optimized support vector machine (SVM) classifier with 10-fold cross validation. Bayesian optimization technique was adopted to minimize the cross-validation loss. From the simulation results, it was inferred that the highest sensitivity, specificity, and classification accuracy of 94.56%, 89.74%, and 92.15% achieved respectively and found to be better than the state-of-the-art approaches. Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.