  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.
