基于Couple熵的抑郁症相干性反馈指标提取

Coherence feedback index extraction of depression based on Couple entropy

为探究抑郁症患者脑网络连通特性及其作为在线反馈指标的可行性。首先,采用对容积导体效应不敏感的相干性虚部(IC)构建脑网络,能够有效便捷的避免虚假连接影响。然后,提取具有显著性差异的IC值作为特征集,提出结合Couple熵(CE)和Relief过滤式特征选择方法优化特征集,结合特征与类、特征之间关系信息提高特征集质量。同时,根据自我参照脑网络模块整合特征集,构造在线反馈指标。最后,采用K最近邻(KNN)、支持向量机(SVM)分类器进行对比分析。结果发现,各频段内CE-Relief特征选择方法提取的特征集最小,且分类准确率均高于90%;Alpha频段IC值识别抑郁效果最好,分类准确率可达到100%;自我参照脑网络的前额区平均IC值分类能力在各频段内具有优势且稳定,分类准确率均高于80%。

In order to explore the connectivity characteristics of the brain network of patients with depression and its feasibility as an online feedback indicator. First, the brain network is constructed using the imaginary part of coherency(IC) that is not sensitive to the volume conductor effect, this can effectively and conveniently avoid the influence of false connections. Then, the IC value with significant difference is extracted as a feature set, and a combination of Couple entropy(CE) and Relief filtering feature selection method is proposed to optimize the feature set, and the relationship information between features and classes, features and features are combined to improve the quality of feature sets. At the same time, according to the self-referencing brain network module integration feature set, online feedback indicators are constructed. Finally, K-nearest neighbor(KNN) and support vector machine(SVM) classifiers are used for comparative analysis. The results found that the feature set extracted by the CE-Relief feature selection method in each frequency band is the smallest, and the classification accuracy is higher than 90%. The IC value of the Alpha frequency band has the best effect in identifying depression, and the classification accuracy can reach 100%. The classification ability of the average IC value of the prefrontal area of the self-reference brain network has advantages and stability in each frequency band, and the classification accuracy is higher than 80%.