Please use this identifier to cite or link to this item:
|Title:||Detection of epileptic spike-wave discharges using SVM|
Al Mamun, A.
|Source:||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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 11, 2017
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.