  Stress is prevalent in many aspects of everyday life including work,
healthcare, and social interactions. Many works have studied handcrafted
features from various bio-signals that are indicators of stress. Recently, deep
learning models have also been proposed to detect stress. Typically, stress
models are trained and validated on the same dataset, often involving one
stressful scenario. However, it is not practical to collect stress data for
every scenario. So, it is crucial to study the generalizability of these models
and determine to what extent they can be used in other scenarios. In this
paper, we explore the generalization capabilities of Electrocardiogram
(ECG)-based deep learning models and models based on handcrafted ECG features,
i.e., Heart Rate Variability (HRV) features. To this end, we train three HRV
models and two deep learning models that use ECG signals as input. We use ECG
signals from two popular stress datasets - WESAD and SWELL-KW - differing in
terms of stressors and recording devices. First, we evaluate the models using
leave-one-subject-out (LOSO) cross-validation using training and validation
samples from the same dataset. Next, we perform a cross-dataset validation of
the models, that is, LOSO models trained on the WESAD dataset are validated
using SWELL-KW samples and vice versa. While deep learning models achieve the
best results on the same dataset, models based on HRV features considerably
outperform them on data from a different dataset. This trend is observed for
all the models on both datasets. Therefore, HRV models are a better choice for
stress recognition in applications that are different from the dataset
scenario. To the best of our knowledge, this is the first work to compare the
cross-dataset generalizability between ECG-based deep learning models and HRV
models.
