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Updated: Jul 1, 2025

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

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Diurnal Pain Classification in Critically Ill Patients using Machine Learning on Accelerometry and Analgesic Data.

Jessica Sena¹, Sabyasachi Bandyopadhyay^2,3, Mohammad Tahsin Mostafiz²

¹Federal University of Minas Gerais/Department of Computer Science, Belo Horizonte, Brazil.

IEEE International Conference on Bioinformatics and Biomedicine Workshops. IEEE International Conference on Bioinformatics and Biomedicine

|March 11, 2024

Summary

This summary is machine-generated.

Wearable accelerometers can predict pain in intensive care unit (ICU) patients using machine learning. This technology aids in identifying painful episodes and can be enhanced with patient data and medication information.

Keywords:

Accelerometer Actigraph CatBoost ICU Intensive Care Unit Machine Learning Shimmer

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Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

Area of Science:

Biomedical Engineering
Critical Care Medicine
Machine Learning Applications

Background:

Pain assessment in intensive care units (ICUs) is difficult due to communication barriers.
Previous studies suggest a link between physical activity and pain in critically ill patients.

Purpose of the Study:

To develop machine learning models using accelerometer data to predict pain levels in ICU patients.
To evaluate the efficacy of wearable sensors in objective pain quantification.

Main Methods:

Trained Logistic Regression, CatBoost, and XG-Boost models on accelerometer data, patient demographics, and pain history.
Classified pain into 'pain vs. no-pain' and severity levels, separately for daytime and nighttime.
Utilized SHAP analysis to identify key predictive features.

Main Results:

Machine learning models accurately predicted pain episodes, with CatBoost showing higher nighttime performance (AUC: 0.82).
Accelerometer features were most important, but demographics and medications also contributed.
Including analgesic data improved classification of pain severity.

Conclusions:

Accelerometer data, patient demographics, and pain history can effectively screen for painful episodes in ICUs.
This approach, augmented with medication data, offers moderate classification of pain severity.
Wearable sensor technology shows promise for objective pain monitoring in critical care.