170056 20260326144912.0 doi 10.1109/TBME.2026.3658304 sideral 148616 ART-2026-148616 eng Jiménez-Ocaña, Alvaro A. Stress Detection Using Heart Rate Variability and Respiratory Signals Derived From a Single-Lead ECG 2026 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. info:eu-repo/grantAgreement/ES/AEI/AEI PID2024-160041OB-I00 info:eu-repo/grantAgreement/ES/DGA/T39-23R info:eu-repo/grantAgreement/ES/MICINN/PID2022-140556OB-I00 info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131106B-I00 info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Pantoja, Andrés Armañac, Pablo Universidad de Zaragoza (orcid)0000-0001-5918-1043 Bailón, Raquel Universidad de Zaragoza (orcid)0000-0003-1272-0550 Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-3434-9254 Giraldo, Luis Felipe 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 5007 075 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ciencia Comput.Intelig.Ar (2026), [12 pp.] IEEE trans. biomed. eng. IEEE Transactions on Biomedical Engineering 0018-9294 2459188 http://zaguan.unizar.es/record/170056/files/texto_completo.pdf Versión publicada 3744290 http://zaguan.unizar.es/record/170056/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:170056 articulos driver 2026-03-26-14:48:26 ARTICLE