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|Title:||Detection of epileptic spike-wave discharges using SVM|
Al Mamun, A.
|Citation:||Pan, Y.,Ge, S.S.,Tang, F.R.,Al Mamun, A. (2007). Detection of epileptic spike-wave discharges using SVM. Proceedings of the IEEE International Conference on Control Applications : 467-472. ScholarBank@NUS Repository. https://doi.org/10.1109/CCA.2007.4389275|
|Abstract:||In this work, support vector machine (SVM) is applied for detecting epileptic spikes and sharp waves in EEG signal. EEG data are obtained from two-channels EEG monitor on Swiss mice. Our technique maps these intracranial electroencephalogram (EEG) time series into corresponding novelty sequences by classifying short-time, energy based statistics computed from one-second windows of data. Numeric simulation studies demonstrate the effect of the SVM detection, and a comparison between SVM and artificial neural network with back-propagation algorithm is presented to show the advantages of SVM algorithm for detecting epileptic spikewave discharge in EEG time series. © 2007 IEEE.|
|Source Title:||Proceedings of the IEEE International Conference on Control Applications|
|Appears in Collections:||Staff Publications|
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