ArticleAmerican journal of physiology. Regulatory, integrative and comparative physiology2021
Objective pain stimulation intensity and pain sensation assessment using machine learning classification and regression based on electrodermal activity.
Article in American journal of physiology. Regulatory, integrative and comparative physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06701461 (Measurement of Osteoarthritic Patient Pain Through Electrodermal Activity Signals), which is not on this map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Measurement of Osteoarthritic Patient Pain Through Electrodermal Activity Signals
Who cites it
19 citing papers in PubMed, 46 citations in OpenAlex.
- Complexity Analysis of Skin Nerve Activity for Quantitative Assessment of Acute Sympathetic Nervous System Activation.Sensors (Basel, Switzerland) · 2026Article
- Pain assessment using physiological responses/markers in different types of pain: a scoping review.NPJ digital medicine · 2026Article
- A tonic electrocutaneous stimulation paradigm for graded sensory C-fiber engagement in healthy subjects.Frontiers in pain research (Lausanne, Switzerland) · 2026Article
- Influence of Pre-Existing Pain on the Body's Response to External Pain Stimuli: Experimental Study.JMIR biomedical engineering · 2025Article
- Optimizing Sensor Locations for Electrodermal Activity Monitoring Using a Wearable Belt System.Journal of sensor and actuator networks · 2025Article
- Defining the need for analgesia in the emergency department: protocol for an international Delphi process.BMJ open · 2025Article
- Article
- Estimation of Pressure Pain in the Lower Limbs Using Electrodermal Activity, Tissue Oxygen Saturation, and Heart Rate Variability.Sensors (Basel, Switzerland) · 2025Article
- Pain Assessment for Patients with Dementia and Communication Impairment: Feasibility Study of the Usage of Artificial Intelligence-Enabled Wearables.Sensors (Basel, Switzerland) · 2024Article
- Resting-state frontal electroencephalography (EEG) biomarkers for detecting the severity of chronic neuropathic pain.Scientific reports · 2024Article
- Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.Cyborg and bionic systems (Washington, D.C.) · 2024Review
- Article
- Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database.Life (Basel, Switzerland) · 2023Article
- Differentiating between stress- and EPT-induced electrodermal activity during dental examination.Computers in biology and medicine · 2023Article
- Stress State Classification Based on Deep Neural Network and Electrodermal Activity Modeling.Sensors (Basel, Switzerland) · 2023Article
- Sympathetic Arousal Detection in Horses Using Electrodermal Activity.Animals : an open access journal from MDPI · 2023Article
- Comparison of Electrodermal Activity from Multiple Body Locations Based on Standard EDA Indices' Quality and Robustness against Motion Artifact.Sensors (Basel, Switzerland) · 2022Article
- Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches.Frontiers in pain research (Lausanne, Switzerland) · 2022Review
- Combining Electrodermal Activity With the Peak-Pain Time to Quantify Three Temporal Regions of Pain Experience.Frontiers in pain research (Lausanne, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
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 complicates pain management and exacerbates the epidemic of opioid abuse. We have tested classification and regression machine learning models to objectively estimate pain sensation in healthy subjects using electrodermal activity (EDA). Twenty-three volunteers underwent pain stimulation using thermal grills. Three different "pain stimulation intensities" were induced for each subject, who reported the "pain sensation" right after each stimulus using a VAS (0-10). EDA data were collected throughout the experiment. For machine learning, we computed validated features of EDA based on time-domain decomposition, spectral analysis, and differential features. Models for estimation of pain stimulation intensity and pain sensation achieved maximum macroaveraged geometric mean scores of 69.7% and 69.2%, respectively, when three classes were considered ("No," "Low," and "High"). Regression of levels of stimulation intensity and pain sensation achieved
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.