Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure
Epilepsy is a neurological disease that’s characterized by perennial seizures. In this neurological condition the transient electrical phenomenon within the brain occurs that produces an amendment in sensation, awareness, and behavior of an individuals that leads to risk. To understand the brain beh...
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my.utm.941612022-02-28T13:24:58Z http://eprints.utm.my/id/eprint/94161/ Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure Sharma, M. K. Ray, K. Yupapin, P. Kaiser, M. S. Ong, C. T. Ali, J. QA Mathematics Epilepsy is a neurological disease that’s characterized by perennial seizures. In this neurological condition the transient electrical phenomenon within the brain occurs that produces an amendment in sensation, awareness, and behavior of an individuals that leads to risk. To understand the brain behavior Electroencephalogram (EEG) signals are used in six different sub-bands viz. Alpha (a ), Beta (ß ), Gamma1 (? 1), Gamma2 (? 2), Theta (? ) and Delta (d ). The Brainstorm software is used for visualizing, analyzing and filtration of EEG signals in each sub-band. This paper deals with the extraction of the various features in each sub-bands and different Machine Learning classifiers were used on these extracted features for comparative analysis in terms of Accuracy, prediction Speed and training time in MatLab. The various statistical and spectral methods are applied on EEG signals to obtained the distinct features in each sub-band. After compared these classifiers on the performance parameters.we have 8 best classifier trained Models that were utilized in checking effectiveness to clearly distinguish between Epileptic and Normal cases. 2020 Conference or Workshop Item PeerReviewed Sharma, M. K. and Ray, K. and Yupapin, P. and Kaiser, M. S. and Ong, C. T. and Ali, J. (2020) Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure. In: 2nd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2020, 17 - 18 December 2020, Savar, Bangladesh. http://dx.doi.org/10.1007/978-981-33-4673-4_17 |
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QA Mathematics Sharma, M. K. Ray, K. Yupapin, P. Kaiser, M. S. Ong, C. T. Ali, J. Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure |
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Epilepsy is a neurological disease that’s characterized by perennial seizures. In this neurological condition the transient electrical phenomenon within the brain occurs that produces an amendment in sensation, awareness, and behavior of an individuals that leads to risk. To understand the brain behavior Electroencephalogram (EEG) signals are used in six different sub-bands viz. Alpha (a ), Beta (ß ), Gamma1 (? 1), Gamma2 (? 2), Theta (? ) and Delta (d ). The Brainstorm software is used for visualizing, analyzing and filtration of EEG signals in each sub-band. This paper deals with the extraction of the various features in each sub-bands and different Machine Learning classifiers were used on these extracted features for comparative analysis in terms of Accuracy, prediction Speed and training time in MatLab. The various statistical and spectral methods are applied on EEG signals to obtained the distinct features in each sub-band. After compared these classifiers on the performance parameters.we have 8 best classifier trained Models that were utilized in checking effectiveness to clearly distinguish between Epileptic and Normal cases. |
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Conference or Workshop Item |
author |
Sharma, M. K. Ray, K. Yupapin, P. Kaiser, M. S. Ong, C. T. Ali, J. |
author_facet |
Sharma, M. K. Ray, K. Yupapin, P. Kaiser, M. S. Ong, C. T. Ali, J. |
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Sharma, M. K. |
title |
Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure |
title_short |
Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure |
title_full |
Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure |
title_fullStr |
Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure |
title_full_unstemmed |
Comparative analysis of different classifiers on EEG signals for predicting epileptic seizure |
title_sort |
comparative analysis of different classifiers on eeg signals for predicting epileptic seizure |
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2020 |
url |
http://eprints.utm.my/id/eprint/94161/ http://dx.doi.org/10.1007/978-981-33-4673-4_17 |
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1726791490307358720 |
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13.160551 |