Why the study?
It was unclear whether machine learning models detecting anxiety and stress using physiological signals learn physiological features specific to stress or general to high-arousal emotions.
Observational (n=97)
Machine learning models trained to detect stress from ECG and EDA signals appear to learn features of generalized high emotional arousal rather than stress-specific physiological patterns.
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.
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Zhou et al. (2023) conducted an observational in Anxiety, stress, and emotional arousal (n=97). Machine learning classification using ECG and EDA features vs. Within-corpus vs. cross-corpus evaluation was evaluated on Cross-corpus classification accuracy and AUC for stress vs. arousal.
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