Chu Wen-Lin, Huang Min-Wei, Jian Bo-Lin, Cheng Kuo-Sheng
Department of Biomedical Engineering, National Cheng Kung University, Tainan, 701 Taiwan.
Department of Psychiatry, Chiayi Branch, Taichung Veterans General Hospital, Chia-Yi, 600 Taiwan.
Ann Gen Psychiatry. 2017 Sep 25;16:34. doi: 10.1186/s12991-017-0157-z. eCollection 2017.
In this study, the international affective picture system was used to evoke emotion, and then the corresponding signals were collected. The features from different points of brainwaves, frequency, and entropy were used to identify normal, moderately, and markedly ill schizophrenic patients.
The signals were collected and preprocessed. Then, the signals were separated according to three types of emotions and five frequency bands. Finally, the features were calculated using three different methods of entropy. For classification, the features were divided into different sections and classification using support vector machine (principal components analysis on 95%). Finally, simple regression and correlation analysis between the total scores of positive and negative syndrome scale and features were used.
At first, we observed that to classify normal and markedly ill schizophrenic patients, the identification result was as high as 81.5%, and therefore, we further explored moderately and markedly ill schizophrenic patients. Second, the identification rate in both moderately and markedly ill schizophrenic patient was as high as 79.5%, which at the Fz point signal in high valence low arousal fragments was calculated using the ApEn methods. Finally, the total scores of positive and negative syndrome scale were used to analyze the correlation with the features that were the five frequency bands at the Fz point signal. The results show that the value was less than .001 at the beta wave in the 15-18 Hz frequency range.
在本研究中，使用国际情感图片系统来唤起情感，然后收集相应信号。利用来自脑电波不同点、频率和熵的特征来识别正常、中度和重度精神分裂症患者。
收集并预处理信号。然后，根据三种情感类型和五个频段对信号进行分离。最后，使用三种不同的熵方法计算特征。为了进行分类，将特征划分为不同部分，并使用支持向量机（95%的主成分分析）进行分类。最后，对阳性和阴性症状量表总分与特征之间进行简单回归和相关性分析。
首先，我们观察到，对于正常和重度精神分裂症患者的分类，识别结果高达81.5%，因此，我们进一步探索中度和重度精神分裂症患者。其次，中度和重度精神分裂症患者的识别率均高达79.5%，这是在使用ApEn方法计算的高价低唤醒片段中的Fz点信号处得出的。最后，使用阳性和阴性症状量表总分来分析与Fz点信号处五个频段的特征的相关性。结果表明，在15 - 18赫兹频率范围内的β波处，p值小于0.001。
