Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity

• Published in: American journal of physiology. Regulatory, integrative and comparative physiology Vol. 321; no. 2; pp. R186 - R196
• Main Authors: Posada–Quintero, Hugo F., Kong, Youngsun, Chon, Ki H.
• Format: Journal Article
• Language: English
• Published: United States American Physiological Society 01.08.2021
• Subjects: Adult; Electrodiagnosis; Feasibility Studies; Female; Galvanic Skin Response; Hot Temperature; Humans; Machine Learning; Male; Pain - diagnosis; Pain - etiology; Pain - physiopathology; Pain - psychology; Pain Measurement; Pain Perception; Pain Threshold; Predictive Value of Tests; Reproducibility of Results; Severity of Illness Index; Signal Processing, Computer-Assisted; Skin - innervation; Sympathetic Nervous System - physiopathology; Young Adult; electrodermal activity; pain; pain stimulation intensity; pain sensation; thermal grill

An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the United States. The current gold standard to quantify pain is highly subjective, based upon self-reporting with numerical or visual analog scale (VAS). This subjectivity...