Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress

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.

Artificial Intelligence in Nutrients Science Research: A Review

“…AI techniques can also be useful in diagnosing mild dehydration. Posada-Quintero et al, using machine learning, investigated the possibility of detecting mild dehydration with autonomic responses to cognitive stress ( n = 17) [ 49 ]. Taking into account the autonomic control indicators based on electrodermal activity (EDA) and pulse rate variability (PRV) in the Stroop test, they obtained 91.2% overall accuracy of mild dehydration detection.…”

“…An important issue in this research area is the assessment of the reliability and credibility of the test results obtained using AI techniques. Another essential issue is the modification of the dietician–patient relationship in the case of replacing, in whole or in part, the work of a medical professional by AI systems [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. The problem of trust in AI-based systems, especially in the elderly, remains open.…”

Overtraining Syndrome as a Complex Systems Phenomenon

“…This modern classification approach was similarly effective in developing a 28variable based model that exhibited 81% accuracy in discriminating severely ill patients with COVID-19 from those with only mild symptoms (Yao et al, 2020). And despite the inherent challenges in detecting mild dehydration, predictive models developed with machine learning performed well in stratification of hydration status prompted by autonomic nervous system responses to cognitive stress (Posada-Quintero et al, 2019). Whereas there was not a clear advantage in using machine learning over traditional statistics in these examples, it's notable that more researchers are learning and applying these modern methods that are becoming more conventional.…”

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

“…The use of deconvolution methods were emphasised to obtain the different features of the EDA signals. Another variable that helps to determine different emotional states in the participants is BVP, which combined with EDA gives very good results in the prediction as well [12,15,20,22,38,48,[56][57][58]. Table 5 shows additional variables.The following major cluster (besides EDA and BVP) includes TMP.…”