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Epileptic Seizure Detection and Classification Using Machine Learning
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Author(s): Rekh Ram Janghel (National Institute of Technology Raipur, India), Yogesh Kumar Rathore (National Institute of Technology Raipur, India)and Gautam Tatiparti (National Institute of Technology Raipur, India)
Copyright: 2019
Pages: 13
Source title:
Early Detection of Neurological Disorders Using Machine Learning Systems
Source Author(s)/Editor(s): Sudip Paul (North-Eastern Hill University Shillong, India), Pallab Bhattacharya (National Institute of Pharmaceutical Education and Research (NIPER) Ahmedabad, India)and Arindam Bit (National Institute of Technology Raipur, India)
DOI: 10.4018/978-1-5225-8567-1.ch009
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Abstract
Epilepsy is a brain ailment identified by unpredictable interruptions of normal brain activity. Around 1% of mankind experience epileptic seizures. Around 10% of the United States population experiences at least a single seizure in their life. Epilepsy is distinguished by the tendency of the brain to generate unexpected bursts of unusual electrical activity that disrupts the normal functioning of the brain. As seizures usually occur rarely and are unforeseeable, seizure recognition systems are recommended for seizure detection during long-term electroencephalography (EEG). In this chapter, ANN models, namely, BPA, RNN, CL, PNN, and LVQ, have been implemented. A prominent dataset was employed to assess the proposed method. The proposed method is capable of achieving an accuracy of 97.5%; the high accuracy obtained has confirmed the great success of the method.
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