Abstract
Epileptic seizure occurs due to neuronal disorder that results in abnormal pattern of brain signal. Electroencephalogram (EEG) signal represents a modest measure of electric flow in a human brain. An EEG is one of the main diagnostic tests for epilepsy. Due to the presence of seizures, normal pattern of brain waves disappears and different other brain waves can be visualized during the recording of EEG. Approximately, 1% of the total population in the world is affected by this disease. This paper is based on a systematic approach for epilepsy detection of human brain by extraction of features and classification of EEG signal. Feature extraction is completed by discrete wavelet transform (DWT) and multilayer perceptron neural network (MLPNN) with deep learning is used for classification. Experimental study of the proposed work is done through Python platform with an encouraging performance.
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Acknowledgements
The first author would like to thank the technical support of the Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore.
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Singh, N., Dehuri, S. (2019). Usage of Deep Learning in Epileptic Seizure Detection Through EEG Signal. In: Nath, V., Mandal, J. (eds) Nanoelectronics, Circuits and Communication Systems . Lecture Notes in Electrical Engineering, vol 511. Springer, Singapore. https://doi.org/10.1007/978-981-13-0776-8_20
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DOI: https://doi.org/10.1007/978-981-13-0776-8_20
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