000076848 001__ 76848
000076848 005__ 20191126134633.0
000076848 0247_ $$2doi$$a10.1016/j.dsp.2018.01.007
000076848 0248_ $$2sideral$$a104553
000076848 037__ $$aART-2018-104553
000076848 041__ $$aeng
000076848 100__ $$0(orcid)0000-0003-3434-9254$$aLaguna, P.$$uUniversidad de Zaragoza
000076848 245__ $$aEigenvalue-based time delay estimation of repetitive biomedical signals
000076848 260__ $$c2018
000076848 5060_ $$aAccess copy available to the general public$$fUnrestricted
000076848 5203_ $$aThe 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.
000076848 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T96$$9info:eu-repo/grantAgreement/ES/ISCIII/CIBER-BBN$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2015-68820-R$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2016-75458-R
000076848 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000076848 590__ $$a2.792$$b2018
000076848 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b104 / 265 = 0.392$$c2018$$dQ2$$eT2
000076848 592__ $$a0.541$$b2018
000076848 593__ $$aSignal Processing$$c2018$$dQ2
000076848 593__ $$aElectrical and Electronic Engineering$$c2018$$dQ2
000076848 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000076848 700__ $$aGarde, A.
000076848 700__ $$aGiraldo, B.F.
000076848 700__ $$aMeste, O.
000076848 700__ $$aJané, R.
000076848 700__ $$aSörnmo, L.
000076848 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000076848 773__ $$g75 (2018), 107-119$$pDigit. signal process.$$tDIGITAL SIGNAL PROCESSING$$x1051-2004
000076848 8564_ $$s653488$$uhttps://zaguan.unizar.es/record/76848/files/texto_completo.pdf$$yPostprint
000076848 8564_ $$s56059$$uhttps://zaguan.unizar.es/record/76848/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000076848 909CO $$ooai:zaguan.unizar.es:76848$$particulos$$pdriver
000076848 951__ $$a2019-11-26-13:42:20
000076848 980__ $$aARTICLE