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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fatemeh Pouromran, Srinivasan Radhakrishnan, Sagar Kamarthi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0ab60366eea4463ca53986027aad20ff
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0ab60366eea4463ca53986027aad20ff
record_format	dspace
spelling	oai:doaj.org-article:0ab60366eea4463ca53986027aad20ff2021-12-02T20:09:20ZExploration of physiological sensors, features, and machine learning models for pain intensity estimation.1932-620310.1371/journal.pone.0254108https://doaj.org/article/0ab60366eea4463ca53986027aad20ff2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254108https://doaj.org/toc/1932-6203In 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.Fatemeh PouromranSrinivasan RadhakrishnanSagar KamarthiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254108 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fatemeh Pouromran Srinivasan Radhakrishnan Sagar Kamarthi Exploration of physiological sensors, features, and machine learning models for pain intensity estimation.
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
author	Fatemeh Pouromran Srinivasan Radhakrishnan Sagar Kamarthi
author_facet	Fatemeh Pouromran Srinivasan Radhakrishnan Sagar Kamarthi
author_sort	Fatemeh Pouromran
title	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_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_sort	exploration of physiological sensors, features, and machine learning models for pain intensity estimation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0ab60366eea4463ca53986027aad20ff
work_keys_str_mv	AT fatemehpouromran explorationofphysiologicalsensorsfeaturesandmachinelearningmodelsforpainintensityestimation AT srinivasanradhakrishnan explorationofphysiologicalsensorsfeaturesandmachinelearningmodelsforpainintensityestimation AT sagarkamarthi explorationofphysiologicalsensorsfeaturesandmachinelearningmodelsforpainintensityestimation
_version_	1718375043921084416

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

Ejemplares similares