Abstract
ElectroEncephaloGram (EEG) signals play an important role to identify epileptic disorders. Epilepsy is a neurological disorder that is an unexpected electrical disruption of the brain, because the activity of nerve cells in the brain becomes disrupted, causing people to experience “seizures.” Nowa-day, researcher works and focuses on automatic analysis of EEG signals to classify epilepsy. The EEG signal recording system produces very long data. Thus, the classification of epileptic seizures requires a time-consuming process. This paper proposes a Support Vector Machine (SVM)-based automated seizure classification system using Approximation Entropy (ApEn). ApEn reduces patient data size without loss of information. ApEn is a statistical parameter that measures the amplitude value of an EEG signal current based on its previous amplitude value. In this paper, we measure sensitivity, specificity, and accuracy using SVM classifiers. The overall score as high as 98.62% can be achieved by using the proposed system to distinguish the epilepsy state (seizure class) from the normal state (non-seizure class) using the time domain method.
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Gabani, H.B., Paunwala, C.N. (2018). EEG Signal Classification for Epileptogenic Zone and Seizure Zone. In: Kher, R., Gondaliya, D., Bhesaniya, M., Ladid, L., Atiquzzaman, M. (eds) Proceedings of the International Conference on Intelligent Systems and Signal Processing . Advances in Intelligent Systems and Computing, vol 671. Springer, Singapore. https://doi.org/10.1007/978-981-10-6977-2_5
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