Novel Optimization Models for Abnormal Brain Activity Classification
成果类型：
Article
署名作者：
Chaovalitwongse, W. Art; Fan, Ya-Ju; Sachdeo, Rajesh C.
署名单位：
Rutgers University System; Rutgers University New Brunswick; Jersey Shore University Medical Center
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.1080.0573
发表日期：
2008
页码：
1450-1460
关键词：
摘要：
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.