Lee Y J, Zhu Y S, Xu Y H, Shen M F, Zhang H X, Thakor N V
Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China.
Clin Neurophysiol. 2001 Jul;112(7):1288-94. doi: 10.1016/s1388-2457(01)00544-2.
The aim of this study is to detect non-linearity in the EEG of schizophrenia with a modified method of surrogate data. We also want to identify if dimension complexity (correlation dimension using spatial embedding) could be used as a discriminating statistic to demonstrate non-linearity in the EEG. The difference between the attractor dimension of healthy subjects and schizophrenic subjects is expected to be interpreted as reflecting some mechanisms underlying brain wave by views of non-linear dynamics analysis may reflect mechanistic differences.
EEGs were recorded with 14 electrodes in 18 healthy male subjects (average age: 26.3; range: 20--35) and 18 male schizophrenic patients (average age: 30.6; range: 24--40) during a resting eye-closed state. Neither of two groups was taking medicines. All artificial epochs in the EEG records were rejected by an experienced doctor's visual inspection.
Testing non-linearity with modified surrogate data, we showed that correlation dimension of EEG data of schizophrenia does refuse the null hypothesis that the data were resulted from a linear dynamic system. A decrease of dimension complexity was found in the EEG of schizophrenia compared with controls. We interpreted it as the result of the psychopath's dysfunction overall brain. The surrogating procedure results in a significant increase in D(s).
Non-linearity of the EEG in schizophrenia was proven in our study. We think the correlation dimension with spatial embedding as a good discriminating statistic for testing such non-linearity. Moreover, schizophrenic patients' EEGs were compared with controls and a lower dimension complexity was found. The results of our study indicate the possibility of using the methods of non-linear time series analysis to identify the EEGs of schizophrenic patients.
本研究旨在采用改进的替代数据方法检测精神分裂症患者脑电图(EEG)中的非线性。我们还想确定维度复杂性(使用空间嵌入的关联维数)是否可作为一种判别统计量来证明EEG中的非线性。从非线性动力学分析的角度来看,健康受试者和精神分裂症患者吸引子维度的差异有望被解释为反映脑电波潜在机制的差异,这可能反映了机制上的不同。
在18名健康男性受试者(平均年龄:26.3岁;范围:20 - 35岁)和18名男性精神分裂症患者(平均年龄:30.6岁;范围:24 - 40岁)闭眼休息状态下,用14个电极记录EEG。两组均未服药。EEG记录中的所有人造片段均经经验丰富的医生目视检查后剔除。
使用改进的替代数据测试非线性,我们发现精神分裂症患者EEG数据的关联维数确实拒绝了数据来自线性动态系统的原假设。与对照组相比,精神分裂症患者的EEG中发现维度复杂性降低。我们将其解释为精神疾病患者全脑功能障碍的结果。替代过程导致D(s)显著增加。
我们的研究证明了精神分裂症患者EEG的非线性。我们认为使用空间嵌入的关联维数是测试这种非线性的良好判别统计量。此外,将精神分裂症患者的EEG与对照组进行比较,发现其维度复杂性较低。我们的研究结果表明,使用非线性时间序列分析方法识别精神分裂症患者EEG的可能性。