Classification of schizophrenia using Genetic Algorithm-Support Vector Machine (GA-SVM)
Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages
6047-6050
Date Issued
2013
Author(s)
Hiesh, M.-H.
Lam Andy, Y.-Y.
Shen, C.-P.
Chen, W.
Lin, F.-S.
Sung, H.-Y.
Lin, J.-W.
Abstract
Recently, Event-Related Potential (ERP) has being the most popular method in evaluating brain waves of schizophrenia patients. ERP is one of the electroencephalography (EEG), which is measured the change of brain waves after giving patients certain stimulations instead of resting state. However, with traditional statistical analysis method, both P50 and MMN showed significant difference between controls and patients but not in Gamma band. Gamma band is a 30-50 Hz auditory stimulation which had been suggested may be abnormal in schizophrenia patients. Our data are recruited from 5 schizophrenia patients and 5 controls in National Taiwan University Hospital have been tested with this platform. The results showed that detection rate is 88.24% and we also analyzed the importance of features, including Standard Deviation (SD) and Total Variation (TotalVar) in different stage of wavelet transform. Therefore, this proposed methodology could serve as a valuable clinical decision support for physiologists in evaluating schizophrenia. ? 2013 IEEE.
Other Subjects
Auditory stimulation; Clinical decision support; Different stages; Event-related potentials; National Taiwan University; Schizophrenia patients; Standard deviation; Statistical analysis methods; Decision support systems; Electrophysiology; Support vector machines; Diseases; algorithm; auditory stimulation; case control study; computer simulation; electroencephalogram; electroencephalography; evoked response; human; pathophysiology; physiology; schizophrenia; support vector machine; Taiwan; wavelet analysis; Acoustic Stimulation; Algorithms; Brain Waves; Case-Control Studies; Computer Simulation; Electroencephalography; Evoked Potentials; Humans; Schizophrenia; Support Vector Machines; Taiwan; Wavelet Analysis
Type
conference paper