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.

中文翻译：

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使用脑电信号自动检测精神分裂症的计算机化方法

精神分裂症（SZ）的诊断传统上是由熟练的精神科医生通过患者的访谈进行的。该过程是耗时，繁重的，容易出错和偏见。因此，本研究的目的是开发一种使用脑电图（EEG）信号的自动SZ识别方案，该方案可以消除上述问题并为临床医生和研究人员提供支持。本研究介绍了一种涉及经验模态分解（EMD）技术的方法设计，用于根据EEG信号诊断SZ，以完美处理非平稳和非线性EEG信号的行为。在这项研究中，每个EEG信号通过EMD算法分解为固有模式函数（IMF），然后从这些IMF计算22个统计特征/特征。其中，应用Kruskal Wallis检验，选择了五个特征作为重要特征。通过SZ EEG数据集的几个著名分类器测试了获得的功能集的性能。在考虑的分类器中，整体袋装树是对SZ进行正确分类的最佳分类器，对SZ的正确分类率为93.21％，对于IMF 2的总体准确度为89.59％。这些结果表明，EEG信号有效地将SZ患者与健康对照（HC）受试者区分开并有可能成为心理医生支持SZ阳性诊断的工具。