Abstract：The aims of this paper were to investigate the power spectral entropy of the resting state EGG of schizophrenic (SC) and depressive (DP) patients and compare the performance of this index in these two kinds of diseases and to explore the reflection of this index on brain abnormalities of the two diseases. The subjects included 100 schizophrenia patients and 100 depression patients whose sex and age were matched (male: 50, female: 50). EEG was recorded under two conditions: (i) resting with eyes closed, and (ii) resting with eyes open. The signal preprocessing and the artifact correction were performed first. Power spectrum analysis was based on Welch transform, and the entropy of power spectrum was calculated by using relative power after normalization. At last, statistical analysis of the index was done by t-test and analysis of variance (ANOVA) through Matlab and SPSS. In any of the same states, the group average power spectral entropy of patients with schizophrenia was lower than that of patients with depression (lead average power spectrum entropy: closed-eye state: SC:1.26; DP:1.32; open eyes state: SC:1.33; DP:1.37), and the difference was significant on most leads (P<0.05). For all the subjects, the power spectral entropy in the closed-eye state was lower than that in the open eyes state. The decrease of the entropy from closed-eye state to open eyes state showed significant difference between the two groups in Fp1 and Fp2 leads (P<0.05) (Fp1:SC:0.08; DP:0.02;Fp2:SC:0.09; DP:0.02). In the closed-eye state, there was a difference in the asymmetry of power spectrum entropy between the left and right hemispheres of patients with schizophrenia and depression, and the schizophrenic group showed wider asymmetry (the pairs of electrodes with significant asymmetry: SC: 4 pairs, including F3-F4, O1-O2, F7-F8, T5-T6; DP: 2 pairs, including P3-P4, F7-F8). In the open eyes state, both of the two groups only showed significant asymmetry in the F7-F8, T5-T6 leads (P<0.05). In conclusion, the power spectrum entropy can sensitively and intuitively describe the distribution and irregularity of the power spectrum, and thus reflect the complexity of the EEG signal and the individual brain activity. The index could be used as an effective reference to distinguish between schizophrenia and depression, reflecting the difference in brain activity between the two diseases in the resting state.
冯静雯, 赖虹宇, 邓伟, 曾金坤, 张军鹏, 李涛. 精神分裂症和抑郁症患者静息态脑电功率谱熵的对照研究[J]. 中国生物医学工程学报, 2019, 38(4): 385-391.
Feng Jingwen, Lai Hongyu, Deng Wei, Zeng Jinkun, Zhang Junpeng, Li Tao. The Comparative Study of Resting State EEG’s Power Spectral Entropy Between Schizophrenic and Depressive Patients. Chinese Journal of Biomedical Engineering, 2019, 38(4): 385-391.