关键词: 脑电信号（EEG）, 抑郁症, 频谱不对称分析（SASI）, 去趋势波动分析（DFA）

Abstract:

Based on the 64-channel EEG acquisition system of EGI Company, the EEG data of 16 adolescent patients with depression and 16 healthy patients with eyes closed for 4 minutes at resting-state data are collected. Secondly, Spectral Asymmetry Index（SASI） and Detrended Fluctuation Analysis（DFA） algorithm are applied to extract EEG time-frequency domain features. For the extracted features from the channel, on the hand, the features from the single optimal electrode Pz are selected as the channel of classification; on the other hand, the features from the multi-channels selected by Genetic Algorithm（GA） are used for classification. Finally, the Support Vector Machine（SVM） is applied to a single channel and multi-channels to classify depression patients and healthy people. The classification accuracies of the single channel are 45.5% and 51.5%, respectively, while the classification accuracies of the multi-channels selected by GA are 78.1% and 90.6%, respectively. Experimental results show that SASI algorithm is better than that of DFA real-time algorithm of computing, and the DFA algorithm accuracy is better than SASI algorithm. This study provides a theoretical basis for computer aided diagnosis of depression.

Key words: Electroencephalogram（EEG）, depression, Spectral Asymmetry Index（SASI）, Detrended Fluctuation Analysis（DFA）