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An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.

Philip Gouverneur1, Aleksandra Badura2, Frédéric Li3

  • 1Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany. philipgouverneur@gmx.de.

Scientific Data
|September 27, 2024
PubMed
Summary

This study introduces the PainMonit Dataset for machine learning-based automated pain detection using physiological signals. The dataset aids research by providing crucial data for developing better pain recognition algorithms.

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Pain Research

Background:

  • Machine learning requires large datasets, but data scarcity hinders automated pain recognition research.
  • Current pain monitoring relies on self-reports, with limited publicly available datasets for physiological data-based algorithms.

Purpose of the Study:

  • To introduce the PainMonit Dataset, a novel resource for advancing automated pain detection using physiological signals.
  • To facilitate the development and validation of machine learning models for objective pain assessment.

Main Methods:

  • The PainMonit Dataset comprises two parts: heat-induced pain in 55 healthy subjects and physiotherapy-related pain in 49 participants.
  • Physiological data, including BVP, EDA, skin temperature, ECG, EMG, IBI, HR, and respiration, were recorded using multiple sensor modalities.

Main Results:

  • The dataset captures diverse physiological responses associated with experimentally induced and clinically relevant pain scenarios.
  • It offers a valuable resource for training and testing machine learning algorithms for automated pain recognition.

Conclusions:

  • The PainMonit Dataset addresses the critical need for accessible, high-quality physiological data in pain research.
  • This resource is expected to accelerate progress in developing more accurate and objective methods for pain monitoring.