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

•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 class...