document: Support feature machine for classification of abnormal brain activity In this study , a novel multidimensional time series classification technique , namely support feature machine ( SFM ) , is proposed . SFM is inspired by the optimization model of support vector machine and the nearest neighbor rule to incorporate both spatial and temporal of the multi-dimensional time series data . This paper also describes an application of SFM for detecting abnormal brain activity . Epilepsy is a case in point in this study . In epilepsy studies , electroencephalograms ( EEGs ) , acquired in multidimensional time series format , have been traditionally used as a gold-standard tool for capturing the electrical changes in the brain . From multi-dimensional EEG time series data , SFM was used to identify seizure pre-cursors and detect seizure susceptibility ( pre-seizure ) periods . The empirical results showed that SFM achieved over 80 % correct classification of per-seizure EEG on average in 10 patients using 5-fold cross validation . The proposed optimization model of SFM is very compact and scalable , and can be implemented as an online algorithm . The outcome of this study suggests that it is possible to construct a computerized algorithm used to detect seizure pre-cursors and warn of impending seizures through EEG classification .
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