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Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier.

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Title: Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier.
Authors: Hasan, Md. Kamrul1, Ahamed, Md. Asif1, Ahmad, Mohiuddin1, Rashid, M. A.2
Source: Applied Bionics & Biomechanics. 8/13/2017, p1-12. 12p.
Subjects: Diagnosis of epilepsy, Electroencephalography, Electrophysiology, Feature extraction, K-nearest neighbor classification
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Applied Bionics & Biomechanics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using k-NN Classifier.
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  Data: <searchLink fieldCode="JN" term="%22Applied+Bionics+%26+Biomechanics%22">Applied Bionics & Biomechanics</searchLink>. 8/13/2017, p1-12. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Diagnosis+of+epilepsy%22">Diagnosis of epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Electrophysiology%22">Electrophysiology</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink>
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Bionics & Biomechanics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/2017/6848014
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      – Code: eng
        Text: English
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      – SubjectFull: Electrophysiology
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      – SubjectFull: K-nearest neighbor classification
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              M: 08
              Text: 8/13/2017
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