Novel Optimization Models for Abnormal Brain Activity Classification

Abstract

This paper proposes a new classification technique, called support feature machine (SFM), for multidimensional time-series data. The proposed technique was applied to the classification of abnormal brain activity represented in electroencephalograms (EEGs). First, the dynamical properties of EEGs from each electrode were extracted. These dynamical profiles were put in SFM, which is an optimization model that maximizes classification accuracy by selecting electrodes (features) that correctly classify unlabeled EEG samples based on the nearest-neighbor classification rule. The empirical studies were performed on the EEG data sets collected from 10 subjects. The performance of SFM was assessed and compared with the ones achieved by the traditional k-nearest-neighbor classifier and support vector machines (SVMs). The results show that SFM achieved, on average, over 90% correct classification and outperformed other classification techniques. In the validation step, SFM correctly classified unseen preseizure and normal EEGs with over 73% accuracy.