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Analysis of EEG entropy during visual evocation of emotion in schizophrenia

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 patien...

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Autores principales: Chu, Wen-Lin, Huang, Min-Wei, Jian, Bo-Lin, Cheng, Kuo-Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613505/
https://www.ncbi.nlm.nih.gov/pubmed/29021815
http://dx.doi.org/10.1186/s12991-017-0157-z
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author Chu, Wen-Lin
Huang, Min-Wei
Jian, Bo-Lin
Cheng, Kuo-Sheng
author_facet Chu, Wen-Lin
Huang, Min-Wei
Jian, Bo-Lin
Cheng, Kuo-Sheng
author_sort Chu, Wen-Lin
collection PubMed
description 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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spelling pubmed-56135052017-10-11 Analysis of EEG entropy during visual evocation of emotion in schizophrenia Chu, Wen-Lin Huang, Min-Wei Jian, Bo-Lin Cheng, Kuo-Sheng Ann Gen Psychiatry Primary Research 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. BioMed Central 2017-09-25 /pmc/articles/PMC5613505/ /pubmed/29021815 http://dx.doi.org/10.1186/s12991-017-0157-z Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Primary Research
Chu, Wen-Lin
Huang, Min-Wei
Jian, Bo-Lin
Cheng, Kuo-Sheng
Analysis of EEG entropy during visual evocation of emotion in schizophrenia
title Analysis of EEG entropy during visual evocation of emotion in schizophrenia
title_full Analysis of EEG entropy during visual evocation of emotion in schizophrenia
title_fullStr Analysis of EEG entropy during visual evocation of emotion in schizophrenia
title_full_unstemmed Analysis of EEG entropy during visual evocation of emotion in schizophrenia
title_short Analysis of EEG entropy during visual evocation of emotion in schizophrenia
title_sort analysis of eeg entropy during visual evocation of emotion in schizophrenia
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613505/
https://www.ncbi.nlm.nih.gov/pubmed/29021815
http://dx.doi.org/10.1186/s12991-017-0157-z
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