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Epileptic Seizure Detection and Classification Using Machine Learning

Epileptic Seizure Detection and Classification Using Machine Learning

Rekh Ram Janghel, Yogesh Kumar Rathore, Gautam Tatiparti
Copyright: © 2019 |Pages: 13
ISBN13: 9781522585671|ISBN10: 1522585672|EISBN13: 9781522585688
DOI: 10.4018/978-1-5225-8567-1.ch009
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MLA

Janghel, Rekh Ram, et al. "Epileptic Seizure Detection and Classification Using Machine Learning." Early Detection of Neurological Disorders Using Machine Learning Systems, edited by Sudip Paul, et al., IGI Global, 2019, pp. 152-164. https://doi.org/10.4018/978-1-5225-8567-1.ch009

APA

Janghel, R. R., Rathore, Y. K., & Tatiparti, G. (2019). Epileptic Seizure Detection and Classification Using Machine Learning. In S. Paul, P. Bhattacharya, & A. Bit (Eds.), Early Detection of Neurological Disorders Using Machine Learning Systems (pp. 152-164). IGI Global. https://doi.org/10.4018/978-1-5225-8567-1.ch009

Chicago

Janghel, Rekh Ram, Yogesh Kumar Rathore, and Gautam Tatiparti. "Epileptic Seizure Detection and Classification Using Machine Learning." In Early Detection of Neurological Disorders Using Machine Learning Systems, edited by Sudip Paul, Pallab Bhattacharya, and Arindam Bit, 152-164. Hershey, PA: IGI Global, 2019. https://doi.org/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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