76848 20191126134633.0 doi 10.1016/j.dsp.2018.01.007 sideral 104553 ART-2018-104553 eng (orcid)0000-0003-3434-9254 Laguna, P. Universidad de Zaragoza Eigenvalue-based time delay estimation of repetitive biomedical signals 2018 Access copy available to the general public Unrestricted The time delay estimation problem associated with an ensemble of misaligned, repetitive signals is revisited. Each observed signal is assumed to be composed of an unknown, deterministic signal corrupted by Gaussian, white noise. This paper shows that maximum likelihood (ML) time delay estimation can be viewed as the maximization of an eigenvalue ratio, where the eigenvalues are obtained from the ensemble correlation matrix. A suboptimal, one-step time delay estimate is proposed for initialization of the ML estimator, based on one of the eigenvectors of the inter-signal correlation matrix. With this approach, the ML estimates can be determined without the need for an intermediate estimate of the underlying, unknown signal. Based on respiratory flow signals, simulations show that the variance of the time delay estimation error for the eigenvalue-based method is almost the same as that of the ML estimator. Initializing the maximization with the one-step estimates, rather than using the ML estimator alone, the computation time is reduced by a factor of 5M when using brute force maximization (M denoting the number of signals in the ensemble), and a factor of about 1.5 when using particle swarm maximization. It is concluded that eigenanalysis of the ensemble correlation matrix not only provides valuable insight on how signal energy, jitter, and noise influence the estimation process, but it also leads to a one-step estimator which can make the way for a substantial reduction in computation time. info:eu-repo/grantAgreement/ES/DGA/T96 info:eu-repo/grantAgreement/ES/ISCIII/CIBER-BBN info:eu-repo/grantAgreement/ES/MINECO/DPI2015-68820-R info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 2.792 2018 ENGINEERING, ELECTRICAL & ELECTRONIC 104 / 265 = 0.392 2018 Q2 T2 0.541 2018 Signal Processing 2018 Q2 Electrical and Electronic Engineering 2018 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Garde, A. Giraldo, B.F. Meste, O. Jané, R. Sörnmo, L. 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 75 (2018), 107-119 Digit. signal process. DIGITAL SIGNAL PROCESSING 1051-2004 653488 http://zaguan.unizar.es/record/76848/files/texto_completo.pdf Postprint 56059 http://zaguan.unizar.es/record/76848/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:76848 articulos driver 2019-11-26-13:42:20 ARTICLE