Features of Mismatch Negativity for Classification of Schizophrenia Patients

• Main Authors: Chia-Wei Chieh, 解家威
• Other Authors: Hsiao-Wen Chung
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/43723378382540545713

碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 103 === An auditory oddball paradigm can elicit the mismatch negativity (MMN) waveform with characteristic waveform at about 100 ms after the stimulus onset. There was also a characteristic waveform at about 300 ms after either standard or deviant sounds presentation. In this study, we used classification tools to find compositions of features in the hope of devising and imaging biomarkers to differentiate between schizophrenia patients and healthy subjects. Specifically event-related potentials (ERP) were measured from 113 schizophrenia patients and 95 healthy controls using an auditory oddball paradigm. The mean amplitudes and peak latencies of the ERP elicited by standard and deviant sounds at about 100 ms and 300 ms were first calculated. We also used discrete wavelet transform to characterize features the MMN ERP’s. We found that the standard deviation and the energy of the ERP waveform in theta band were significantly different between schizophrenia and control groups. Using support vector machine (SVM) as the classifier, we compared the accuracy of identifying schizophrenics. More accurate classification when MMN waveform is described in the signal-to-noise ratio (SNR) unit. Using a linear kernel in SVM gave higher classification accuracy than using a Gaussian kernel. The proposed wavelet time series feature compositions did not improve the classification accuracy. We consider this study is our first attempt to differentiate between healthy and schizophrenic patients using auditory MMN ERP.