Multivariate analysis of the EEG signal for the detection of Schizophrenia condition is proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose the EEG signal into Intrinsic Mode Functions (IMF) signal. The randomness measure of the IMF signal is determined by computing the entropy of the signal. Five entropy measures such as Approximate entropy, Sample entropy, Permutation entropy, Spectral entropy, and Singular Value Decomposition entropy are measured from the IMF signal. These entropy measures showed a significant difference (p < 0.01) between the healthy controls (HC) and Schizophrenia (SZ) subjects. Many state-of-the-art (SoA) machine learning classifiers are trained on the feature matrix obtained from entropy values of the IMF signal, amongst them Support Vector Machine based on Radial Basis Function (SVM-RBF) provided the highest accuracy and F1-score of 93% for the 95 features. The area under the curve (AUC) value of 0.9831 was obtained using this classifier. These performance metrics suggests that computation of randomness measure such as entropy in the multivariate IMF domain provided better discriminating power in detection of Schizophrenia condition from the multichannel EEG signal.

本文提出了用于检测精神分裂症状况的脑电信号的多元分析。多元经验模式分解（MEMD）用于将EEG信号分解为固有模式函数（IMF）信号。IMF信号的随机性度量是通过计算信号的熵来确定的。从IMF信号中测量了五种熵度量，例如近似熵，样本熵，置换熵，谱熵和奇异值分解熵。这些熵测度显示出显着差异（p 健康对照（HC）和精神分裂症（SZ）受试者之间的<0.01）。在从IMF信号的熵值获得的特征矩阵上训练了许多最新（SoA）机器学习分类器，其中基于径向基函数（SVM-RBF）的支持向量机提供了最高的准确性，并且F1 -95个功能的得分为93％。使用该分类器获得的曲线下面积（AUC）值为0.9831。这些性能指标表明，在多变量IMF域中对诸如熵之类的随机性度量进行计算，可以从多通道EEG信号中更好地识别精神分裂症。