title: Influence of delay time on regularity estimation for voice pathology detection creator: Godino Llorente, Juan Ignacio creator: Gómez García, J.A. creator: Castellanos Dominguez, G. subject: Not determined subject: Robotics and Industrial Computing description: The employment of nonlinear analysis techniques for automatic voice pathology detection systems has gained popularity due to the ability of such techniques for dealing with the underlying nonlinear phenomena. On this respect, characterization using nonlinear analysis typically employs the classical Correlation Dimension and the largest Lyapunov Exponent, as well as some regularity quantifiers computing the system predictability. Mostly, regularity features highly depend on a correct choosing of some parameters. One of those, the delay time �, is usually fixed to be 1. Nonetheless, it has been stated that a unity � can not avoid linear correlation of the time series and hence, may not correctly capture system nonlinearities. Therefore, present work studies the influence of the � parameter on the estimation of regularity features. Three � estimations are considered: the baseline value 1; a � based on the Average Automutual Information criterion; and � chosen from the embedding window. Testing results obtained for pathological voice suggest that an improved accuracy might be obtained by using a � value different from 1, as it accounts for the underlying nonlinearities of the voice signal. publisher: E.U.I.T. Telecomunicación (UPM) rights: https://creativecommons.org/licenses/by-nc-nd/3.0/es/ date: 2012 type: info:eu-repo/semantics/conferenceObject type: Presentation at Congress or Conference source: 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012 | 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012 | 28/08/2012 al 1/09/2012 | San Diego (California) type: PeerReviewed format: application/pdf language: eng rights: info:eu-repo/semantics/openAccess identifier: https://oa.upm.es/20301/