Analysis of EEG entropy during visual evocation of emotion in schizophrenia

Wen Lin Chu, Min Wei Huang, Bo Lin Jian, Kuo-Sheng Cheng

研究成果: Article

6 引文 (Scopus)

摘要

Background: 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. Methods: 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. Results: 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 p value was less than.001 at the beta wave in the 15-18 Hz frequency range.

原文English
文章編號34
期刊Annals of General Psychiatry
16
發行號1
DOIs
出版狀態Published - 2017 九月 25

指紋

Entropy
Electroencephalography
Schizophrenia
Emotions
Brain Waves
Principal Component Analysis
Arousal
Regression Analysis

All Science Journal Classification (ASJC) codes

  • Psychiatry and Mental health

引用此文

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abstract = "Background: 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. Methods: 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. Results: 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 p value was less than.001 at the beta wave in the 15-18 Hz frequency range.",
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Analysis of EEG entropy during visual evocation of emotion in schizophrenia. / Chu, Wen Lin; Huang, Min Wei; Jian, Bo Lin; Cheng, Kuo-Sheng.

於: Annals of General Psychiatry, 卷 16, 編號 1, 34, 25.09.2017.

研究成果: Article

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