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首页> 外文会议>International conference on advances in computing, communications and informatics >EDA wavelet features as Social Anxiety Disorder (SAD) estimator in adolescent females
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EDA wavelet features as Social Anxiety Disorder (SAD) estimator in adolescent females

机译：EDA小波特征是青春期女性的社交焦虑症（SAD）估算器

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Social Anxiety Disorder(SAD) effects individual's social behaviour and results in excessive self-consciousness, negative judgmental thoughts and uncontrollable fear. It is visible not only in behavior but also pattern of physiological signals (such as electrodermal activity) of individuals as it is associated with autonomic nervous system (ANS). Previous studies have used various features of Electrodermal Activity (EDA) such as Mean SCR, Min SCR, Range, Slope and Max SCL etc to distinguish between groups of anxious and control group subject during rest and anxious task/situations. This research explores the use of EDA wavelet features to estimate the social anxiety disorder of female subjects via Multi Layer Perceptron (MLP). In this study joint time-frequency domain features of EDA signal via wavelet analysis were extracted. The Backward regression model with p收起
机译：社交焦虑症（SAD）影响个人的社交行为，并导致过度的自我意识，消极的判断思想和无法控制的恐惧。由于它与自主神经系统（ANS）有关，因此不仅在行为方面可见，而且在个体的生理信号（如皮肤电活动）模式中也可见。先前的研究已经使用了皮肤电活动（EDA）的各种功能，例如平均SCR，最小SCR，范围，斜率和最大SCL等，以区分休息和焦虑任务/情况下的焦虑组和对照组。这项研究探索了利用EDA小波特征通过多层感知器（MLP）评估女性受试者的社交焦虑症。在本研究中，通过小波分析提取了EDA信号的联合时频域特征。具有p收起的Backward回归模型

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