Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms

• Published in: Expert systems with applications Vol. 113; pp. 18 - 32
• Main Authors: Raghu, Shivarudhrappa, Sriraam, Natarajan
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2018; Elsevier BV
• Subjects: Algorithms; Artificial intelligence; Automated detection; Automatic identification; Classification; Classifiers; Convulsions & seizures; Diagnostic systems; Electroencephalography; Epilepsy; Epileptic seizures; Expert systems; Feature extraction; Feature selection; Focal EEG; Human error; Machine learning; Neighborhood component analysis; Regularization; Seizing; Seizures; Software development tools; Support vector machines; Automated detection; Feature selection; Epileptic seizures; Neighborhood component analysis; Focal EEG
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2018.06.031

•CADFES tool is designed for automated classification of focal seizures•Significant features were selected using neighbourhood component analysis•Regularization parameter was optimized to ensure less classification loss•Classification accuracy of 96.1% was attained using support vector machine classifier•Results were better than existing approaches. Background: Classification and localization of focal epileptic seizures provide a proper diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long-term electroencephalography (EEG) is tedious, time-consuming and leads to human error. Therefore, there is a need for an automated classification system. Methods: In this paper, we introduce a tool called CADFES: computerized automated detection of focal epileptic seizures. For the study, total 41.66 hours of EEG data from the Bern-Barcelona database was used. Set of 28 features were extracted from time, frequency, and statistical domain and significant features were selected using neighborhood component analysis (NCA). In NCA, optimization of regularization parameter ensured better classification accuracy (less classification loss) with seven features. The performance of the algorithm was assessed using support vector machine (SVM), K-nearest neighbor (K-NN), random forest and adaptive boosting (AdaBoost) classifiers. Results: Experimental results revealed sensitivity, specificity, accuracy, positive predictive rate, negative predictive rate, and area under the curve of 97.6%, 94.4%, 96.1%, 92.9%, 98.8% and 0.96 respectively using the SVM classifier. Finally, MATLAB based software tool referred to as CADFES was introduced for automated classification of focal and non-focal seizures. Comparison results ensure that proposed study is superior to existing methods. Hence, it is expected to perform better at the hospitals for automated classification of focal epileptic seizures in real-time.