Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier
Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually re...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2017/6848014 |
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| author | Md. Kamrul Hasan Md. Asif Ahamed Mohiuddin Ahmad M. A. Rashid |
| author_facet | Md. Kamrul Hasan Md. Asif Ahamed Mohiuddin Ahmad M. A. Rashid |
| author_sort | Md. Kamrul Hasan |
| collection | DOAJ |
| description | Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistical analysis of EEG signal that is capable of recognizing epileptic seizure with a high degree of accuracy and helps to provide automatic detection of epileptic seizure for different ages of epilepsy. To accomplish the target research, we extract various epileptic features namely approximate entropy (ApEn), standard deviation (SD), standard error (SE), modified mean absolute value (MMAV), roll-off (R), and zero crossing (ZC) from the epileptic signal. The k-nearest neighbours (k-NN) algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to the age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG. |
| format | Article |
| id | doaj-art-b76a1daf8efd44bbbac0902793661142 |
| institution | Kabale University |
| issn | 1176-2322 1754-2103 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-b76a1daf8efd44bbbac09027936611422025-02-03T05:48:00ZengWileyApplied Bionics and Biomechanics1176-23221754-21032017-01-01201710.1155/2017/68480146848014Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN ClassifierMd. Kamrul Hasan0Md. Asif Ahamed1Mohiuddin Ahmad2M. A. Rashid3Department of EEE, Khulna University of Engineering & Technology (KUET), Khulna 9203, BangladeshDepartment of EEE, Khulna University of Engineering & Technology (KUET), Khulna 9203, BangladeshDepartment of EEE, Khulna University of Engineering & Technology (KUET), Khulna 9203, BangladeshFSTK, University Sultan Zainal Abidin (UniSZA), 21300 Kuala Terengganu, Terengganu, MalaysiaElectroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistical analysis of EEG signal that is capable of recognizing epileptic seizure with a high degree of accuracy and helps to provide automatic detection of epileptic seizure for different ages of epilepsy. To accomplish the target research, we extract various epileptic features namely approximate entropy (ApEn), standard deviation (SD), standard error (SE), modified mean absolute value (MMAV), roll-off (R), and zero crossing (ZC) from the epileptic signal. The k-nearest neighbours (k-NN) algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to the age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG.http://dx.doi.org/10.1155/2017/6848014 |
| spellingShingle | Md. Kamrul Hasan Md. Asif Ahamed Mohiuddin Ahmad M. A. Rashid Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier Applied Bionics and Biomechanics |
| title | Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier |
| title_full | Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier |
| title_fullStr | Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier |
| title_full_unstemmed | Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier |
| title_short | Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier |
| title_sort | prediction of epileptic seizure by analysing time series eeg signal using k nn classifier |
| url | http://dx.doi.org/10.1155/2017/6848014 |
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