Optimisation and data mining techniques for the screening of epileptic patients
Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as ElectroEncephaloGrams (EEGs), is extremely challenging. We propose a novel Multi-Dimensional Time Series (MDTS) classification technique, called Connectivity Support Vector Machines (C-SVMs) that integrates brain connectivity network with SVMs. To alter noise in EEG data, Independent Component Analysis based on the Unbiased Quasi Newton Method was applied. C-SVM achieved 94.8% accuracy classifying subjects compared to 69.4% accuracy with standard SVMs. It suggests that C-SVM can be a rapid, yet accurate, technique for online differentiation between epileptic and normal subjects. It may solve other classification MDTS problems too.
Medical Subject Headings
Computational Biology (methods); Electroencephalography (methods); Epilepsy (diagnosis); Humans; Information Storage and Retrieval (methods); Signal Processing, Computer-Assisted
International journal of bioinformatics research and applications
Digital Object Identifier (DOI)
Fan, Ya-Ju; Chaovalitwongse, Wanpracha A.; Liu, Chang-Chia; Sachdeo, Rajesh C.; Iasemidis, Leonidas; and Pardalos, Panos, "Optimisation and data mining techniques for the screening of epileptic patients" (2009). Translational Neuroscience. 1161.