Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-square support vector machine

Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-square support vector machine

Background and objectives: Epilepsy is the most prevalent neurological disorder in humans which is characterized by recurrent seizures resulting in neurologic, cognitive, psychological and social consequences. In addition, the automatic identification of brain conditions can be helpful in eye-brain-...

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Bibliographic Details
Main Authors: Laurent Chanel Djoufack Nkengfack, Daniel Tchiotsop, Romain Atangana, Valérie Louis-Door, Didier Wolf
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:Informatics in Medicine Unlocked
Subjects:
Automated classification system
Epileptic seizures detection
Eye states identification
Electroencephalogram (EEG) signals
Jacobi polynomial transforms (JPTs)
Measures of complexity
Online Access:http://www.sciencedirect.com/science/article/pii/S2352914821000265

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http://www.sciencedirect.com/science/article/pii/S2352914821000265

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