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.

Epilepsy is one of the most common brain disorders, but the dynamical transitions to neurological dysfunctions of epilepsy are not well understood in current neuroscience research. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this study is to develop and present a novel classification technique that is used to classify normal and abnormal (epileptic) brain activities through quantitative analyses of electroencephalogram (EEG) recordings. Such technique is based on the integration of sophisticated approaches from data mining and signal processing research (i.e., chaos theory,k-nearest neighbor, and statistical time series analysis). The proposed technique can correctly classify normal and abnormal EEGs with a sensitivity of 81.29% and a specificity of 72.86%, on average, across ten patients. Experimental results suggest that the proposed technique can be used to develop abnormal brain activity classification for detecting seizure precursors. Success of this study demonstrates that the proposed technique can excavate hidden patterns/relationships in EEGs and give greater understanding of brain functions from a system perspective, which will advance current diagnosis and treatment of epilepsy.