Abstract
Epilepsy is a disorder of the neurology characterized by epileptic seizures. Epileptic seizure can be analyzed via the normal and abnormal brain activity. This anomalous activity can only be observed by using an efficient algorithm. An effective algorithm method also uses signal processing, in which an epileptic signal can be considered as an input signal. This paper introduces an epileptic signal detection technique and compares the characteristics of the brain signals at different stages. Our algorithm is based on the signal processing techniques used in the EEG signal to detect epilepsy. We present a computer-aided automatic detection and classification method for the focal and non-focal EEG signal in this article. Dual Tree Complex Wavelet Transform (DT-CWT) decomposes the EEG signal, and the characteristics are determined from the decomposed coefficients. These functions are trained and classified using classifier ANN and CANFIS. The proposed system achieves 99% accuracy for EEG signal classification. The experimental results are presented to demonstrate the effectiveness of the proposed classification method for classifying the EEG focal and non-focal signals.
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Mariam Bee, M.K., Vidhya, K. (2021). Epileptic Seizure Severity Analysis Using Artificial Neural Network and CANFIS Classification Approach. In: Tavares, J.M.R.S., Chakrabarti, S., Bhattacharya, A., Ghatak, S. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164. Springer, Singapore. https://doi.org/10.1007/978-981-15-9774-9_53
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DOI: https://doi.org/10.1007/978-981-15-9774-9_53
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