Abstract
Dental pain invokes the sympathetic nervous system, which can be measured by electrodermal activity (EDA). In the dental clinic, accurate quantification of pain is needed because it could enable optimized drug-dose treatments, thereby potentially reducing drug addiction. However, a confounding factor is that during pain there is also lingering residual stress, hence, both contribute to the EDA response. Therefore, we investigated whether EDA can differentiate stress from pain during dental examination. The use of electrical pulp test (EPT) is an ideal approach to tease out the dynamics of stress and mimic pain with lingering residual stress. Once the electrical sensation is felt and reaches a critical current threshold, the subject removes the probe from their tooth, hence, this stage of data represents largely EPT stimulus and the residual stress-induced EDA response is smaller. EPT was performed on necrotic and vital teeth in fifty-one subjects. We defined four different data groups of reactions based on each individual's EPT intensity level expectation based on the visual analog scale (VAS) of their baseline trial, as follows: mild stress, mild stress + EPT, strong stress, and strong stress + EPT. EDA-derived features exhibited significant difference between residual lingering stress + EPT groups and stress groups. We obtained 84.6% accuracy with 76.2% sensitivity and 86.8% specificity with multilayer perceptron in differentiating between pure-stress groups vs. stress + EPT groups. Moreover, EPT induced much greater EDA amplitude and faster response than stress. Our finding suggests that our machine learning approach can discriminate between stress and EPT stimulation in EDA signals.