Cargando…
Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress
The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects (n = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine indices of autonomic control based on electrodermal...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7019291/ https://www.ncbi.nlm.nih.gov/pubmed/31877912 http://dx.doi.org/10.3390/nu12010042 |
_version_ | 1783497491033882624 |
---|---|
author | Posada-Quintero, Hugo F. Reljin, Natasa Moutran, Aurelie Georgopalis, Dimitrios Lee, Elaine Choung-Hee Giersch, Gabrielle E. W. Casa, Douglas J. Chon, Ki H. |
author_facet | Posada-Quintero, Hugo F. Reljin, Natasa Moutran, Aurelie Georgopalis, Dimitrios Lee, Elaine Choung-Hee Giersch, Gabrielle E. W. Casa, Douglas J. Chon, Ki H. |
author_sort | Posada-Quintero, Hugo F. |
collection | PubMed |
description | The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects (n = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine indices of autonomic control based on electrodermal activity (EDA) and pulse rate variability (PRV) were obtained during both the rest and test stages of the Stroop task. Measurements were taken on three consecutive days in which subjects were “wet” (not dehydrated) and “dry” (experiencing mild dehydration caused by fluid restriction). Nine approaches were tested for classification of “wet” and “dry” conditions: (1) linear (LDA) and (2) quadratic discriminant analysis (QDA), (3) logistic regression, (4) support vector machines (SVM) with cubic, (5) fine Gaussian kernel, (6) medium Gaussian kernel, (7) a k-nearest neighbor (KNN) classifier, (8) decision trees, and (9) subspace ensemble of KNN classifiers (SE-KNN). The classification models were tested for all possible combinations of the nine indices of autonomic nervous system control, and their performance was assessed by using leave-one-subject-out cross-validation. An overall accuracy of mild dehydration detection was 91.2% when using the cubic SE-KNN and indices obtained only at rest, and the accuracy was 91.2% when using the cubic SVM classifiers and indices obtained only at test. Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers. In summary, measures of autonomic function based on EDA and PRV are suitable for detecting mild dehydration and could potentially be used for the noninvasive testing of dehydration. |
format | Online Article Text |
id | pubmed-7019291 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70192912020-03-04 Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress Posada-Quintero, Hugo F. Reljin, Natasa Moutran, Aurelie Georgopalis, Dimitrios Lee, Elaine Choung-Hee Giersch, Gabrielle E. W. Casa, Douglas J. Chon, Ki H. Nutrients Article The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects (n = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine indices of autonomic control based on electrodermal activity (EDA) and pulse rate variability (PRV) were obtained during both the rest and test stages of the Stroop task. Measurements were taken on three consecutive days in which subjects were “wet” (not dehydrated) and “dry” (experiencing mild dehydration caused by fluid restriction). Nine approaches were tested for classification of “wet” and “dry” conditions: (1) linear (LDA) and (2) quadratic discriminant analysis (QDA), (3) logistic regression, (4) support vector machines (SVM) with cubic, (5) fine Gaussian kernel, (6) medium Gaussian kernel, (7) a k-nearest neighbor (KNN) classifier, (8) decision trees, and (9) subspace ensemble of KNN classifiers (SE-KNN). The classification models were tested for all possible combinations of the nine indices of autonomic nervous system control, and their performance was assessed by using leave-one-subject-out cross-validation. An overall accuracy of mild dehydration detection was 91.2% when using the cubic SE-KNN and indices obtained only at rest, and the accuracy was 91.2% when using the cubic SVM classifiers and indices obtained only at test. Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers. In summary, measures of autonomic function based on EDA and PRV are suitable for detecting mild dehydration and could potentially be used for the noninvasive testing of dehydration. MDPI 2019-12-23 /pmc/articles/PMC7019291/ /pubmed/31877912 http://dx.doi.org/10.3390/nu12010042 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Posada-Quintero, Hugo F. Reljin, Natasa Moutran, Aurelie Georgopalis, Dimitrios Lee, Elaine Choung-Hee Giersch, Gabrielle E. W. Casa, Douglas J. Chon, Ki H. Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress |
title | Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress |
title_full | Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress |
title_fullStr | Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress |
title_full_unstemmed | Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress |
title_short | Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress |
title_sort | mild dehydration identification using machine learning to assess autonomic responses to cognitive stress |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7019291/ https://www.ncbi.nlm.nih.gov/pubmed/31877912 http://dx.doi.org/10.3390/nu12010042 |
work_keys_str_mv | AT posadaquinterohugof milddehydrationidentificationusingmachinelearningtoassessautonomicresponsestocognitivestress AT reljinnatasa milddehydrationidentificationusingmachinelearningtoassessautonomicresponsestocognitivestress AT moutranaurelie milddehydrationidentificationusingmachinelearningtoassessautonomicresponsestocognitivestress AT georgopalisdimitrios milddehydrationidentificationusingmachinelearningtoassessautonomicresponsestocognitivestress AT leeelainechounghee milddehydrationidentificationusingmachinelearningtoassessautonomicresponsestocognitivestress AT gierschgabrielleew milddehydrationidentificationusingmachinelearningtoassessautonomicresponsestocognitivestress AT casadouglasj milddehydrationidentificationusingmachinelearningtoassessautonomicresponsestocognitivestress AT chonkih milddehydrationidentificationusingmachinelearningtoassessautonomicresponsestocognitivestress |