Akar Saime A, Kara Sadik, Agambayev Sumeyra, Bilgic Vedat
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7410-3. doi: 10.1109/EMBC.2015.7320104.
Major depressive disorder (MDD) is a psychiatric mood disorder characterized by cognitive and functional impairments in attention, concentration, learning and memory. In order to investigate and understand its underlying neural activities and pathophysiology, EEG methodologies can be used. In this study, we estimated the nonlinearity features of EEG in MDD patients to assess the dynamical properties underlying the frontal and parietal brain activity. EEG data were obtained from 16 patients and 15 matched healthy controls. A wavelet-chaos methodology was used for data analysis. First, EEGs of subjects were decomposed into 5 EEG sub-bands by discrete wavelet transform. Then, both the Katz's and Higuchi's fractal dimensions (KFD and HFD) were calculated as complexity measures for full-band and sub-bands EEGs. Last, two-way analyses of variances were used to test EEG complexity differences on each fractality measures. As a result, a significantly increased complexity was found in both parietal and frontal regions of MDD patients. This significantly increased complexity was observed not only in full-band activity but also in beta and gamma sub-bands of EEG. The findings of the present study indicate the possibility of using the wavelet-chaos methodology to discriminate the EEGs of MDD patients from healthy controls.
重度抑郁症（MDD）是一种精神性情绪障碍，其特征为在注意力、专注力、学习和记忆方面存在认知和功能损害。为了研究和理解其潜在的神经活动及病理生理学，可以使用脑电图（EEG）方法。在本研究中，我们估计了MDD患者脑电图的非线性特征，以评估额叶和顶叶脑活动背后的动力学特性。脑电图数据取自16名患者和15名匹配的健康对照者。采用小波-混沌方法进行数据分析。首先，通过离散小波变换将受试者的脑电图分解为5个脑电亚带。然后，计算Katz分形维数（KFD）和Higuchi分形维数（HFD），作为全频带和亚带脑电图的复杂性度量。最后，使用双向方差分析来检验每种分形度量下脑电图复杂性的差异。结果发现，MDD患者的顶叶和额叶区域复杂性均显著增加。这种显著增加的复杂性不仅在全频带活动中观察到，在脑电图的β和γ亚带中也观察到。本研究结果表明，使用小波-混沌方法区分MDD患者和健康对照者脑电图具有可能性。
