Siuly Siuly, Khare Smith K, Bajaj Varun, Wang Hua, Zhang Yanchun
IEEE Trans Neural Syst Rehabil Eng. 2020 Nov;28(11):2390-2400. doi: 10.1109/TNSRE.2020.3022715. Epub 2020 Nov 6.
Diagnosis of schizophrenia (SZ) is traditionally performed through patient's interviews by a skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias. Hence the aim of this study is to develop an automatic SZ identification scheme using electroencephalogram (EEG) signals that can eradicate the aforementioned problems and support clinicians and researchers. This study introduces a methodology design involving empirical mode decomposition (EMD) technique for diagnosis of SZ from EEG signals to perfectly handle the behavior of non-stationary and nonlinear EEG signals. In this study, each EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and then twenty-two statistical characteristics/features are calculated from these IMFs. Among them, five features are selected as significant feature applying Kruskal Wallis test. The performance of the obtained feature set is tested through several renowned classifierson a SZ EEG dataset. Among the considered classifiers, theensemble bagged tree performed as the best classifier producing 93.21% correct classification rate for SZ, with an overall accuracy of 89.59% for IMF 2. These results indicate that EEG signals discriminate SZ patients from healthy control (HC) subjects efficiently and have the potential to become a tool for the psychiatrist to support the positive diagnosis of SZ.
精神分裂症（SZ）的诊断传统上是由经验丰富的精神科医生通过对患者进行访谈来完成的。这个过程既耗时又繁琐，还容易出现错误和偏差。因此，本研究的目的是开发一种利用脑电图（EEG）信号的自动SZ识别方案，以消除上述问题，并为临床医生和研究人员提供支持。本研究介绍了一种方法设计，该设计涉及经验模态分解（EMD）技术，用于从EEG信号中诊断SZ，以完美处理非平稳和非线性EEG信号的特性。在本研究中，每个EEG信号通过EMD算法分解为固有模态函数（IMF），然后从这些IMF中计算出二十二个统计特征。其中，应用Kruskal Wallis检验选择了五个特征作为显著特征。通过在一个SZ EEG数据集上使用几种著名的分类器对获得的特征集的性能进行了测试。在所考虑的分类器中，集成袋装树作为最佳分类器，对SZ的正确分类率为93.21%，IMF 2的总体准确率为89.59%。这些结果表明，EEG信号能够有效地将SZ患者与健康对照（HC）受试者区分开来，并且有潜力成为精神科医生支持SZ阳性诊断的一种工具。
