000170056 001__ 170056
000170056 005__ 20260316092630.0
000170056 0247_ $$2doi$$a10.1109/TBME.2026.3658304
000170056 0248_ $$2sideral$$a148616
000170056 037__ $$aART-2026-148616
000170056 041__ $$aeng
000170056 100__ $$aJiménez-Ocaña, Alvaro A.
000170056 245__ $$aStress Detection Using Heart Rate Variability and Respiratory Signals Derived From a Single-Lead ECG
000170056 260__ $$c2026
000170056 5203_ $$aStress 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.
000170056 536__ $$9info:eu-repo/grantAgreement/ES/AEI/AEI PID2024-160041OB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T39-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-140556OB-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131106B-I00
000170056 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000170056 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170056 700__ $$aPantoja, Andrés
000170056 700__ $$0(orcid)0000-0001-5918-1043$$aArmañac, Pablo$$uUniversidad de Zaragoza
000170056 700__ $$0(orcid)0000-0003-1272-0550$$aBailón, Raquel$$uUniversidad de Zaragoza
000170056 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000170056 700__ $$aGiraldo, Luis Felipe
000170056 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000170056 7102_ $$15007$$2075$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ciencia Comput.Intelig.Ar
000170056 773__ $$g(2026), [12 pp.]$$pIEEE trans. biomed. eng.$$tIEEE Transactions on Biomedical Engineering$$x0018-9294
000170056 8564_ $$s2459188$$uhttps://zaguan.unizar.es/record/170056/files/texto_completo.pdf$$yVersión publicada
000170056 8564_ $$s3744290$$uhttps://zaguan.unizar.es/record/170056/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170056 909CO $$ooai:zaguan.unizar.es:170056$$particulos$$pdriver
000170056 951__ $$a2026-03-16-08:17:44
000170056 980__ $$aARTICLE