EDA wavelet features as Social Anxiety Disorder (SAD) estimator in adolescent females

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<0.05 was used in this study for feature selection. The machine learning algorithm developed in this research was able to classify the SAD with accuracy of 82.3% during training, 85.7% during testing and 80% in holdout cases.