000135960 001__ 135960
000135960 005__ 20241220120716.0
000135960 0247_ $$2doi$$a10.1109/TBME.2024.3410008
000135960 0248_ $$2sideral$$a138977
000135960 037__ $$aART-2024-138977
000135960 041__ $$aeng
000135960 100__ $$0(orcid)0000-0002-8334-4786$$aPérez, C.$$uUniversidad de Zaragoza
000135960 245__ $$aPerformance evaluation of QT-RR adaptation time lag estimation in exercise stress testing
000135960 260__ $$c2024
000135960 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135960 5203_ $$aBackground: Slower adaptation of the QT interval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag. Methods: The time lag estimation is based on the delay between the observed QT intervals and the QT intervals derived from the observed RR intervals using a memoryless transformation. Assuming that the two types of QT interval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time lag estimators are derived. Estimation performance is evaluated using an ECG simulator which models change in RR and QT intervals with a known time lag, muscle noise level, respiratory rate, and more. The accuracy of T-wave end delineation and the influence of the learning window positioning for model parameter estimation are also investigated. Results: Using simulated datasets, the results show that the proposed approach to estimation can be applied to any changes in heart rate trend as long as the frequency content of the trend is below a certain frequency. Moreover, using a proper position of the learning window for exercise so that data compensation reduces the effect of nonstationarity, a lower mean estimation error results for a wide range of time lags. Using a clinical dataset, the Laplacian-based estimator shows a better discrimination between patients grouped according to the risk of suffering from coronary artery disease. Conclusions : Using simulated ECGs, the performance evaluation of the proposed method shows that the estimated time lag agrees well with the true time lag.
000135960 536__ $$9info:eu-repo/grantAgreement/ES/DGA/LMP94_21$$9info:eu-repo/grantAgreement/ES/DGA/T39-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-140556OB-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130459B-I00
000135960 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135960 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135960 700__ $$0(orcid)0000-0002-1960-407X$$aPueyo, E.$$uUniversidad de Zaragoza
000135960 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez, J. P.$$uUniversidad de Zaragoza
000135960 700__ $$aViik, J.
000135960 700__ $$aSornmo, L.
000135960 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, P.$$uUniversidad de Zaragoza
000135960 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000135960 773__ $$g71, 11 (2024), 3170-3180$$pIEEE trans. biomed. eng.$$tIEEE Transactions on Biomedical Engineering$$x0018-9294
000135960 8564_ $$s1882623$$uhttps://zaguan.unizar.es/record/135960/files/texto_completo.pdf$$yVersión publicada
000135960 8564_ $$s3598477$$uhttps://zaguan.unizar.es/record/135960/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135960 909CO $$ooai:zaguan.unizar.es:135960$$particulos$$pdriver
000135960 951__ $$a2024-12-20-12:05:31
000135960 980__ $$aARTICLE