Stress Detection Using Heart Rate Variability and Respiratory Signals Derived From a Single-Lead ECG

Stress detection is a widely studied field due to its significant implications for mental and physical health. While multimodal approaches show promising results, they present challenges related to hardware constraints and computational requirements that limits real time implementation in wearable devices. We propose a hybrid methodology combining feature extraction with ma chine learning (ML) for stress detection, using exclusively single-lead electrocardiogram (ECG) signals from which heart rate variability (HRV) and respiratory signals with their derived features are extracted. We evaluated our approach using the ES3 project database, testing various feature combinations with the XGBoost model. Results demonstrate that incorporating ECG-derived respiratory features significantly improves classification accuracy and computational efficiency compared to traditional HRV-based approaches and deep learning models. Feature importance analysis identified a reduced set of key features, resulting in a more efficient model with superior performance and substantially lower inference times than deep learning models. These findings support the feasibility of acute stress detection using a single-lead ECG-based multimodal approach that combines feature extraction with ML techniques, providing insights into stress-induced physiological responses and contributing to more accessible biomedical monitoring strategies.