A Multimodal Autonomic Sensing Framework for Objective Assessment of Patient Responses to Dental Pulp Stimulation

2026-09-28

AI Summary

This study addresses the clinical challenge of subjectivity and difficult objective quantification of pain responses during pulp testing by proposing a multimodal autonomic nervous system sensing framework. The framework employs temporal convolutional networks to extract features from electrocardiogram-derived skin nerve activity (SKNA), R-R intervals (RRI), and electrodermal activity (EDA) signals, while leveraging attention mechanisms for intermediate-level multi-source feature fusion. Additionally, covariates such as anxiety scores are incorporated to enhance individualized assessment accuracy. Experimental results demonstrate that the model achieves balanced accuracies of 80.2% and 60.0% for binary and three-class classification, respectively. Notably, EDA contributes most significantly to performance, while SKNA effectively improves overall accuracy. These findings validate the feasibility of non-invasive, objective quantification of dental pulp pain responses.

Abstract

Patient responses to dental pulp testing, ranging from no sensation to intense pain, provide important information for assessing pulp status in endodontic diagnosis. However, pain is a subjective sensory and emotional experience that varies considerably across individuals and can be difficult to communicate. We investigated whether complementary autonomic signals could support objective assessment of responses during dental examination. Forty-nine patients underwent cold pulp testing, yielding no-response, mild-response, and intense-response conditions. The framework integrated ECG-derived skin nerve activity (SKNA) and R-R intervals (RRI), together with electrodermal activity (EDA), using temporal convolutional network encoders with attention-based mid-level fusion. Individual baseline signals and subject-level covariates, including anxiety scores and biological sex, were also incorporated. The framework achieved 80.2% balanced accuracy, 75.2% sensitivity, and 85.2% specificity for binary classification of no response versus mild or intense response. For three-class classification, it achieved 60.0% balanced accuracy and a 58.8% macro-averaged F1 score. Ablation and attention-weight analyses indicated that EDA contributed most strongly to model performance, followed by RRI, while SKNA improved balanced accuracy by approximately five percentage points. Age was significantly associated with model performance. These findings support the feasibility of multimodal autonomic sensing for objective, non-invasive assessment of responses to dental pulp stimulation.

Problem

• dental pulp testing
• pain assessment
• autonomic signals
• objective evaluation
• multimodal sensing

Innovation

• Multimodal Fusion
• Temporal Convolutional Network
• Attention Mechanism
• Autonomic Sensing
• Dental Pulp Testing