EEG entropy analysis on visual stimulation-evoked emotion and MR images in schizophrenia patients

Abstract

Patients with schizophrenia are typical mental disorder Patients suffer from symptoms such as hallucination and delusion and the society also need to burden highly financial cost for its treatment at the same time Currently there are many publications that discussed the treatment diagnosis prognosis and damage in schizophrenia Among them the electroencephalography (EEG) is an important tool that can be used to be a functional neurological examination to diagnose brain dysfunction caused by non-structural brain lesions and magnetic resonance imaging (MRI) is a non-invasive research and analysis tool used by many experts and researchers to obtain images within the brain Our findings could lead to the development of parameters or biomarkers for psychiatric symptoms in the magnetic resonance imaging study We collected schizophrenia patients and normal controls groups by EEG and MRI For the part of the EEG the International Affective Picture System (IAPS) was used to evoke emotion and then the corresponding signals were collected The features from different points of brainwaves frequency and entropy were used to identify normal moderately and markedly ill schizophrenic patients For the part of the MRI we used VBM to separate brain images to grey matter and white matter than mapping to Montreal Neurological Institute (MNI) space; After that calibration images by DARTEL Adoption joint source-based morphometry (jICA) to analysis of two groups in the grey matter and white matter images Finally we used brain structure images and the error rate of the Wisconsin Card Sorting Test (WCST) as features in support vector machine (SVM) learning and classification The results indicate that the signal analysis method proposed in this study can provide reference information that can be used to determine the phases of schizophrenia symptoms in clinical applications

Date of Award	2018 Feb 6
Original language	English
Supervisor	Kuo-Sheng Cheng (Supervisor)