# Investigating the Generalizability of Physiological Characteristics of Anxiety

arXiv: https://arxiv.org/abs/2402.15513 · HTML: https://mlsanity.com/paper/2402.15513/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 51 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [A dataset of continuous affect annotations and physiological signals for emotion analysis](https://mlsanity.com/paper/1812.02782/) (2018)
- [Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review](https://mlsanity.com/paper/2209.15137/) (2022)
- [GPU-acceleration for Large-scale Tree Boosting](https://mlsanity.com/paper/1706.08359/) (2017)

## Built on by (most cited first, 1 of 1)

- [AffectEval: A Modular and Customizable Framework for Affective Computing](https://mlsanity.com/paper/2504.21184/) (2025)

## Abstract (arXiv)

Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. Specifically, we examine features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from the following three datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. We aim to understand whether these features are specific to anxiety or general to other high-arousal emotions through a statistical regression analysis, in addition to a within-corpus, cross-corpus, and leave-one-corpus-out cross-validation across instances of stress and arousal. We used the following classifiers: Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of the aforementioned models. We found that models trained on an arousal dataset perform relatively well on a previously unseen stress dataset, and vice versa. Our experimental results suggest that the evaluated models may be identifying emotional arousal instead of stress. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection.