Electrodermal Activity as a Potential Diagnostic Biomarker for Alzheimer’s Disease: A Pilot Study

Alzheimer’s disease (AD), the leading cause of dementia, lacks an inexpensive and non-invasive method for early detection. Here, we investigate whether electrodermal activity (EDA), a measure of changes in electrical conductance at the skin’s surface, can offer a convenient avenue for AD screening by capturing the autonomic dysfunction associated with the disease’s pathology. As few studies have comprehensively examined EDA’s utility as a standalone biomarker for AD, we conducted a pilot study with 10 cognitively healthy controls and 20 patients with AD, evaluating EDA recordings collected during an orthostatic test and a Montreal Cognitive Assessment (MoCA). The AD group included both carriers from Colombian kindreds affected by autosomal-dominant AD and patients with sporadic late-onset AD. We compared EDA features between groups using statistical analysis and tested whether machine learning models could separate the two groups using these features. During the MoCA, phasic EDA measures, specifically the number of skin conductance responses (NSSCR) and mean time-variant sympathetic tone (TVSymp), were significantly lower in the AD group than in controls (NSSCR: p=0.0083, Cohen’s d=−1.07; mean TVSymp: p=0.0166, d=−0.96), suggesting a blunted sympathetic response to cognitive stress in AD. No significant between-group differences were observed during the orthostatic test (all p≥0.60). A random forest classifier trained on MoCA-derived EDA features achieved a balanced accuracy of 78% (95% CI 60 to 93%; 75% sensitivity, 80% specificity, AUROC 0.75), with phasic features contributing most to performance. Performance did not differ significantly from that of the support vector machine or logistic regression models. These results provide preliminary, hypothesis-generating evidence for the utility of EDA in screening for AD. The small sample size, unequal group allocation, sex imbalance, and substantial mean age disparity between groups (≈23.5 years) limit the strength of these conclusions and necessitate replication in larger, age- and sex-matched cohorts that include prodromal stages.

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Publication Details

Journal
Sensors
Published
2026-08-27
DOI
https://doi.org/10.3390/s26175407
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00

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article

Electrodermal Activity as a Potential Diagnostic Biomarker for Alzheimer’s Disease: A Pilot Study

Hugo Fernando Posada-Quintero, David Fernando Aguillon, Luís Roberto Mercado Díaz, S Senthur Raja Pandian et al.
Sensors
Emotion and Mood Recognition
article

Electrodermal Activity as a Potential Diagnostic Biomarker for Alzheimer’s Disease: A Pilot Study

Hugo Fernando Posada-Quintero, David Fernando Aguillon, Luís Roberto Mercado Díaz, S Senthur Raja Pandian, Jonathan Ruiz-Triviño
article en

Abstract

Alzheimer’s disease (AD), the leading cause of dementia, lacks an inexpensive and non-invasive method for early detection. Here, we investigate whether electrodermal activity (EDA), a measure of changes in electrical conductance at the skin’s surface, can offer a convenient avenue for AD screening by capturing the autonomic dysfunction associated with the disease’s pathology. As few studies have comprehensively examined EDA’s utility as a standalone biomarker for AD, we conducted a pilot study with 10 cognitively healthy controls and 20 patients with AD, evaluating EDA recordings collected during an orthostatic test and a Montreal Cognitive Assessment (MoCA). The AD group included both carriers from Colombian kindreds affected by autosomal-dominant AD and patients with sporadic late-onset AD. We compared EDA features between groups using statistical analysis and tested whether machine learning models could separate the two groups using these features. During the MoCA, phasic EDA measures, specifically the number of skin conductance responses (NSSCR) and mean time-variant sympathetic tone (TVSymp), were significantly lower in the AD group than in controls (NSSCR: p=0.0083, Cohen’s d=−1.07; mean TVSymp: p=0.0166, d=−0.96), suggesting a blunted sympathetic response to cognitive stress in AD. No significant between-group differences were observed during the orthostatic test (all p≥0.60). A random forest classifier trained on MoCA-derived EDA features achieved a balanced accuracy of 78% (95% CI 60 to 93%; 75% sensitivity, 80% specificity, AUROC 0.75), with phasic features contributing most to performance. Performance did not differ significantly from that of the support vector machine or logistic regression models. These results provide preliminary, hypothesis-generating evidence for the utility of EDA in screening for AD. The small sample size, unequal group allocation, sex imbalance, and substantial mean age disparity between groups (≈23.5 years) limit the strength of these conclusions and necessitate replication in larger, age- and sex-matched cohorts that include prodromal stages.

SensorsVol. 26(17)
University of Connecticut (US), Universidad de Antioquia (CO)
National Institute of Diabetes and Digestive and Kidney Diseases
Openalex Percentile: Top 7%
Emotion and Mood Recognition
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