Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction

Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non- stationary signals like EEG. In this work, SV...

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Hauptverfasser:	Panda, R, Khobragade, P S, Jambhule, P D, Jengthe, S N, Pal, P R, Gandhi, T K
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Adaptation model Biological system modeling Classification accuracy Computational modeling Computers Decomposition trees Design-expert Discrete Wavelet Transform (DWT) Discrete wavelet transforms EEG signals Electroencephalogram (EEGs) Electroencephalography Epileptic Epileptic seizures Feature extraction and classification Healthy subjects Human health sciences Medicine (all) Neurologie Neurology Non-linear Nonstationary signals Open condition Sciences de la santé humaine Seizure Signal processing technique Standard deviation Support Vector Machine (SVM) Support vector machines SVM(support vector machine)
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