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Mild dehydration identification using ML on autonomic responses to cognitive stress

Hugo F. Posada-Quintero, Natasa Reljin, Aurelie Moutran, Dimitrios Georgopalis, Elaine Choung-Hee Lee, Gabrielle E. W. Giersch +2 more
Nutrients2019CC BY 4.0
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Published articlePublication date: Dec 23, 2019Added to atlas: Oct 13, 2026Source
Citations27Crossref referencesChecked Oct 16, 2026
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Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.

This study tested whether mild dehydration can be detected from how the autonomic nervous system responds to cognitive stress. The authors tried every combination of nine autonomic indices with several classifiers, using leave-one-subject-out validation. Accuracy reached 91.2% using indices from rest only or test only, and 86.8% using the change from rest to test.

  • Signals: EDA
  • Result: 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.
  • Result: Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers.
At a glance
Venue
Nutrients
Published
2019
DOI
10.3390/nu12010042 (opens in a new tab)
arXiv
Not reported
PMID
Not reported
Licence
CC BY 4.0 (opens in a new tab)
Signals
Not reported
Form factor
Not reported
Subjects
N = 17
Code
Not reported
Added
Oct 13, 2026

Abstract

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.

Verbatim fromthe publisher's DOI record· Publisher licence: https://creativecommons.org/licenses/by/4.0/

Mild dehydration identification using ML on autonomic responses to cognitive stress: ChatPPG research summary card

Key results

91.2%
Accuracy
Accuracy reached 91.2% using indices from rest only or test only, and 86.8% using the change from rest to test.

How it works

How Method works: Setup → Method → ResultSetup: Cohort (N = 17); Signals (EDA). Method: Method. Result: Result (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.); Result (Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers.).SetupCohort: N = 17CohortN = 17Signals: EDASignalsEDAMethodMethodResultResult: 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.ResultAn overall accuracy of mild dehydration…Result: Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers.ResultAccuracy was 86.8% when rest-to-test…
Cohort
N = 17

Cite

@article{milddehydrationedaprvsim2019,
  title = {{Mild dehydration identification using ML on autonomic responses to cognitive stress}},
  author = {Hugo F. Posada-Quintero and Natasa Reljin and Aurelie Moutran and Dimitrios Georgopalis and Elaine Choung-Hee Lee and Gabrielle E. W. Giersch and Douglas J. Casa and Ki H. Chon},
  year = {2019},
  journal = {Nutrients},
  doi = {10.3390/nu12010042}
}

History

  1. Added to the ChatPPG research hub

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