Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.
认知功能损害是精神分裂症的三大原发症状之一，在疾病早期发现和高危人群风险预警等方面具有重要价值。为了研究精神分裂症患者在认知负载状态下的脑电图特异性，本试验收集 17 例精神分裂症患者和 19 例健康受试者的脑电信号作为对照，基于小波变换提取各频段信号，计算非线性动力学及脑功能网络属性等特征，并利用机器学习算法将两类人群进行自动分类分析。试验结果表明，两组受试者在认知负载状态下，Fp1 和 Fp2 导联在 α、β、θ、γ 这 4 个频带的关联维数和样本熵的差异均具有统计学意义，提示大脑额叶功能损伤是精神分裂症认知功能损害的重要原因。进一步基于机器学习的自动分类分析结果表明，将非线性动力学与脑功能网络属性相结合作为分类器的输入特征，所得分类效果最优，其结果显示准确率为 76.77%、敏感度为 72.09%、特异性为 80.36%。本研究结果表明，脑电信号的非线性动力学和脑功能网络属性等特征，或可作为精神分裂症早期筛查和辅助诊断的潜在生物标记物。.
Keywords: cognitive impairment; electroencephalogram; functional brain network; machine learning; schizophrenia.