Real Time Computer Aided Detection System for the Prediction of Clinical Antidepressant Responses

• Main Authors: Sophia Ina Wu, 吳伊娜
• Other Authors: 陳中平
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/bztjg8

碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 105 === Major depressive disorder (MDD) is increasingly to be recognized as a chronic, deteriorating illness with the high risk to obtain comorbidity. In order to provide clinicians with a subjective approach to decide appropriate treatments for MDD patients, a real time automatic detection system for predicting the antidepressant responses is of important. Wavelet Transform and nonlinear methods - Largest Lyapunov Exponent (LLE), Detrended Fluctuation Analysis (DFA), Fractal Dimension (FD), Correlation Dimension (CD) and Approximate Entropy (ApEn) were applied to extract the features from electroencephalography (EEG) activities in antidepressant responses. Non-parametric analysis, correlation analysis and confusion matrix were employed to evaluate the performance of classifying and decide the optimal threshold for discrimination. Moreover, the system is built to aid clinicians’ in prediction of the antidepressant responses before treatments by an automatic real time detection system and the results can be viewed within 40 seconds (45X).