Abstract
Epilepsy is a disorder of the central nervous system in which a considerably large number of neurons at a certain instance of time show abnormal electrical activity. EEG or electroencephalogram signals plays a significant role in the diagnosis of epilepsy. Worldwide roughly 50 million people are affected by epilepsy that includes patients from all walks of life. In this paper we have studied the performance of artificial neural network (ANN) on EEG signals (CHB-MIT database) by applying principal component analysis (PCA) for selection of channels. The results reflect the performance of the neural network in different configuration. Out of the 23 channels considered, after using PCA, the highest accuracy of 86.7% is achieved with 18 channels. With the reduction of channels the accuracy decreased simultaneously. The study also brings forth the shortcomings as well as determines the area in this domain that holds prospective for future scope of work.
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Chakrabarti, S., Swetapadma, A., Pattnaik, P.K. (2019). A Channel Selection Method for Epileptic EEG Signals. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-13-1951-8_51
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DOI: https://doi.org/10.1007/978-981-13-1951-8_51
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