Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain...

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Bibliographic Details
Published in:PloS one 2021-07, Vol.16 (7), p.e0254108-e0254108
Main Authors: Pouromran, Fatemeh, Radhakrishnan, Srinivasan, Kamarthi, Sagar
Format: Article
Language:English
Subjects:
Automation
Biology and Life Sciences
Biosensors
Classification
Computer and Information Sciences
Datasets
Deep learning
Diabetes
Discriminant analysis
Drug abuse
Drug addiction
EKG
Electrocardiography
Electromyography
Feature extraction
Heart rate
Industrial engineering
Learning algorithms
Machine learning
Mathematical models
Measurement techniques
Medicine and Health Sciences
Narcotics
Nervous system
Neural networks
Opioids
Pain
Pain perception
Patients
Performance prediction
Physical Sciences
Physiological aspects
Physiology
Regression analysis
Research and Analysis Methods
Root-mean-square errors
Sensors
Signal processing
Skin
Statistical analysis
Support vector machines
Wrist
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Summary:In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0254108