Electroencephalogram signal classification based on shearlet and contourlet transforms |
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| Affiliation: | 1. Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas-SP 13069-901, Brazil;2. Institute of Computing, University of Campinas, Campinas-SP 13083-852, Brazil;1. Department of Computer Science and Engineering, University of Dhaka, Bangladesh;2. ICube Laboratory, University of Strasbourg, France;1. School of Science & Technology, International Hellenic University, Thessaloniki, Greece;2. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece;3. Information Technologies Institute, Centre of Research & Technology Hellas, Thessaloniki, Greece;4. Ubitech Ltd., Athens, Greece;1. LaMOS Research Unit, Department of Operational Research, University of Abderrahmane Mira, Road Targua Ouzemour 06000, Bejaia, Algeria;2. School of Computer Science and Informatics, University of College Dublin (UCD), Belfield, Dublin 4, Ireland |
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| Abstract: | Epilepsy is a disorder that affects approximately 50 million people of all ages, according to World Health Organization (2016), which makes it one of the most common neurological diseases worldwide. Electroencephalogram (EEG) signals have been widely used to detect epilepsy and other brain abnormalities. In this work, we propose and evaluate a novel methodology based on shearlet and contourlet transforms to decompose the EEG signals into frequency bands. A set of features are extracted from these time-frequency coefficients and used as input to different classifiers. Experiments are conducted on a public data set to demonstrate the effectiveness of the proposed classification method. The developed system can help neurophysiologists identify EEG patterns in epilepsy diagnostic tasks. |
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