000109468 001__ 109468
000109468 005__ 20230519145512.0
000109468 0247_ $$2doi$$a10.1109/TBME.2021.3058781
000109468 0248_ $$2sideral$$a125673
000109468 037__ $$aART-2021-125673
000109468 041__ $$aeng
000109468 100__ $$aBock, Carl
000109468 245__ $$aECG Beat Representation and Delineation by Means of Variable Projection
000109468 260__ $$c2021
000109468 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109468 5203_ $$aObjective: The electrocardiogram (ECG) follows a characteristic shape, which has led to the development of several mathematical models for extracting clinically important information. Our main objective is to resolve limitations of previous approaches, that means to simultaneously cope with various noise sources, perform exact beat segmentation, and to retain diagnostically important morphological information. Methods: We therefore propose a model that is based on Hermite and sigmoid functions combined with piecewise polynomial interpolation for exact segmentation and low-dimensional representation of individual ECG beat segments. Hermite and sigmoidal functions enable reliable extraction of important ECG waveform information while the piecewise polynomial interpolation captures noisy signal features like the baseline wander (BLW). For that we use variable projection, which allows the separation of linear and nonlinear morphological variations of the according ECG waveforms. The resulting ECG model simultaneously performs BLW cancellation, beat segmentation, and low-dimensional waveform representation. Results: We demonstrate its BLW denoising and segmentation performance in two experiments, using synthetic and real data. Compared to state-of-the-art algorithms, the experiments showed less diagnostic distortion in case of denoising and a more robust delineation for the P and T wave. Conclusion: This work suggests a novel concept for ECG beat representation, easily adaptable to other biomedical signals with similar shape characteristics, such as blood pressure and evoked potentials. Significance: Our method is able to capture linear and nonlinear wave shape changes. Therefore, it provides a novel methodology to understand the origin of morphological variations caused, for instance, by respiration, medication, and abnormalities.
000109468 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109468 590__ $$a4.756$$b2021
000109468 592__ $$a1.298$$b2021
000109468 594__ $$a9.4$$b2021
000109468 591__ $$aENGINEERING, BIOMEDICAL$$b37 / 98 = 0.378$$c2021$$dQ2$$eT2
000109468 593__ $$aBiomedical Engineering$$c2021$$dQ1
000109468 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109468 700__ $$aKovacs, Peter
000109468 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000109468 700__ $$aMeier, Jens
000109468 700__ $$aHuemer, Mario
000109468 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000109468 773__ $$g68, 10 (2021), 2997-3008$$pIEEE trans. biomed. eng.$$tIEEE Transactions on Biomedical Engineering$$x0018-9294
000109468 8564_ $$s4680193$$uhttps://zaguan.unizar.es/record/109468/files/texto_completo.pdf$$yVersión publicada
000109468 8564_ $$s3895220$$uhttps://zaguan.unizar.es/record/109468/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109468 909CO $$ooai:zaguan.unizar.es:109468$$particulos$$pdriver
000109468 951__ $$a2023-05-18-15:12:43
000109468 980__ $$aARTICLE