Classification of Sensory Nerve Fiber Stimulation Using Electrodermal Activity

Pain is typically viewed as an internal perception that is difficult to objectively measure. Current methods attempt to quantify pain intensity using self-reported scores, but these are often subjective or difficult to administer. Three primary sensory nerve fibers carry pain and tactile information: AP- and Ad-flbers carrying short touch and acute pain, respectively, and C-fibers carrying dull prolonged pain. Electrodermal activity (EDA), previously shown to be highly sensitive to pain and stress responses, may allow us to distinguish these different pain signals to more fully express measured pain. In this work, we use machine learning to classify EDA responses associated with these fibers during sine wave transcutaneous electrical nerve stimulation. Using several derived EDA features, we are able to develop models that obtain high accuracy (>75%) of classification across all three fiber types in this multiclass classification task. The ability to measure fiber-specific activation may help greatly improve pain detection and treatment research.