Analysis of EEG entropy during visual evocation of emotion in schizophrenia

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

doi:10.1186/s12991-017-0157-z

Abstract

BackgroundIn 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.MethodsThe 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.ResultsAt 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.

Highlights

In this study, the international affective picture system was used to evoke emotion, and the corre‐ sponding signals were collected
The analysis of EEG signals is used during functional neurological examinations to assist in the diagnosis of brain dysfunctions caused by nonstructural lesions of the brain such as epilepsy [4], dementia [5], and intellectual developmental disorders [6, 7], as well as in research into schizophrenia and other mental disorders [8]
Classification of different groups based on brainwaves We used the approximate entropy (ApEn) method to calculate the preprocessing signals as features for the classification

Summary

Introduction

The international affective picture system was used to evoke emotion, and the corre‐ sponding signals were collected. The analysis of EEG signals is a noninvasive and nonradioactive tool that can be used for long-term measurements, and plays a very important role in clinical diagnosis. The analysis of EEG signals is used during functional neurological examinations to assist in the diagnosis of brain dysfunctions caused by nonstructural lesions of the brain such as epilepsy [4], dementia [5], and intellectual developmental disorders [6, 7], as well as in research into schizophrenia and other mental disorders [8]. To further understand the response of schizophrenic patients to various types of stimuli, many studies use visual [9, 10] or auditory [8, 11, 12] stimuli to evoke various emotions in schizophrenic patients, and capture and analyze their EEG signals to determine whether the signals are associated with physiological mechanisms or

Methods

Results

Conclusion