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Toward Constructing a Real-time Social Anxiety Evaluation System: Exploring Effective Heart Rate Features

Social anxiety is a negative emotion which may impair the health of the heart and social functioning of an individual. This work analyzes the influence of social anxiety on the autonomic nerve control of the heart in two social exposure events: public speaking and thesis defending. In an experiment of public speaking, 59 human subjects were tested, and 11 conventional heartbeat measures and a heartbeat measure named the range of local Hurst exponents (RLHE) were evaluated for their capabilities to reveal the onset of social anxiety. Two-sample t-test between the baseline data and high anxiety data shows that social anxiety significantly reduces the complexity of the heartbeats. In an experiment of thesis defense, heartbeats data were acquired from nine graduate students. With the combination of three conventional features and the RLHE feature, a support vector machine classifier obtained true positive rate and true negative rate of 84.88 and 97.29 percent in the five-fold cross validation process of binary classification between high anxiety status and low anxiety status; the classifier also realized a generalization accuracy of 81.82 percent in detecting the high anxiety status in the thesis defense. A real-time anxiety monitoring system was established based on the above anxiety detecting method.