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...
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Public Library of Science (PLoS)
2021-01-01
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| author | Fatemeh Pouromran Srinivasan Radhakrishnan Sagar Kamarthi |
| author_facet | Fatemeh Pouromran Srinivasan Radhakrishnan Sagar Kamarthi |
| author_sort | Fatemeh Pouromran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ab60366eea4463ca53986027aad20ff |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-0ab60366eea4463ca53986027aad20ff2025-08-20T02:01:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01167e025410810.1371/journal.pone.0254108Exploration of physiological sensors, features, and machine learning models for pain intensity estimation.Fatemeh PouromranSrinivasan RadhakrishnanSagar KamarthiIn 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0254108&type=printable |
| spellingShingle | Fatemeh Pouromran Srinivasan Radhakrishnan Sagar Kamarthi Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. PLoS ONE |
| title | Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. |
| title_full | Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. |
| title_fullStr | Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. |
| title_full_unstemmed | Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. |
| title_short | Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. |
| title_sort | exploration of physiological sensors features and machine learning models for pain intensity estimation |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0254108&type=printable |
| work_keys_str_mv | AT fatemehpouromran explorationofphysiologicalsensorsfeaturesandmachinelearningmodelsforpainintensityestimation AT srinivasanradhakrishnan explorationofphysiologicalsensorsfeaturesandmachinelearningmodelsforpainintensityestimation AT sagarkamarthi explorationofphysiologicalsensorsfeaturesandmachinelearningmodelsforpainintensityestimation |