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A Quantitative Analysis Method for Objectively Assessing the Depression Mood Status Based on Portable EEG and Self-rating Scale

机译:一种定量分析方法,用于客观地评估便携式脑电图和自评标度的抑郁情绪状态

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In order to recognize the major depressive mood status of inpatients and achieve its daily change information, a POMS-BCN scale was used to rate the mood status. Meanwhile, a personalized quantified model based on portable EEG was built, which aimed at objectively assessing the major depressive mood status for each patient. 6 inpatients were recruited to join the experiment. The Principal Component Analysis method is used to extract first principal component curve from the POMS-BCN data. The feature extraction method is used to extract linear and nonlinear features from portable EEG data. The regression analysis based on Random Forest is adopted to build the personalized quantified model. The principal component analysis result shows that the first principal component curve is able to recognize the major emotional factor and depict its daily change information. Additionally, the expected quantitative value outputted from the personalized quantified model is highly correlated (the absolute value of correlation coefficient 0.7, P-value 0.05) with the actual first principal component data, which implies that the personalized quantified model can give an accurate objective assessment for the major depressive mood status.
机译:为了认识到住院患者的主要抑郁情绪状态并实现其日常变更信息,使用POMS-BCN规模来评估情绪状态。同时,建立了基于便携式闭路的个性化量化模型,旨在客观地评估每位患者的主要抑郁情绪状态。招募了6位的住院性,以加入实验。主成分分析方法用于从POMS-BCN数据中提取第一主组件曲线。特征提取方法用于从便携式EEG数据提取线性和非线性特征。采用基于随机林的回归分析来构建个性化量化模型。主成分分析结果表明,第一主成分曲线能够识别主要的情绪因素并描述其日常变更信息。另外,从个性化量化模型输出的预期定量值高度相关(相关系数0.7,p值0.05)的实际第一主成分数据具有高度相关性(相关系数0.7,p值0.05),这意味着个性化量化模型可以提供准确的客观评估对于主要的抑郁情绪状况。

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