Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 beten 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 re 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.
['Vivek Sharma', 'Neelam Rup Prakash', 'Parveen Kalra']
I am writing a research paper on a idea EDA wavelet features as Social Anxiety Disorder (SAD) estimator in adolescent females
[{"from": "human", "value": "I am writing a research paper on a idea EDA wavelet features as Social Anxiety Disorder (SAD) estimator in adolescent females"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 beten 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 re 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."}]