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Multichannel dynamic modeling of non-Gaussian mixtures

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Multichannel dynamic modeling of non-Gaussian mixtures

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dc.contributor.author Safont Armero, Gonzalo es_ES
dc.contributor.author Salazar Afanador, Addisson es_ES
dc.contributor.author Vergara Domínguez, Luís es_ES
dc.contributor.author Gomez, Enriqueta es_ES
dc.contributor.author Villanueva, Vicente es_ES
dc.date.accessioned 2020-12-04T04:32:13Z
dc.date.available 2020-12-04T04:32:13Z
dc.date.issued 2019-09 es_ES
dc.identifier.issn 0031-3203 es_ES
dc.identifier.uri http://hdl.handle.net/10251/156422
dc.description.abstract [EN] This paper presents a novel method that combines coupled hidden Markov models (HMM) and non Gaussian mixture models based on independent component analyzer mixture models (ICAMM). The proposed method models the joint behavior of a number of synchronized sequential independent component analyzer mixture models (SICAMM), thus we have named it generalized SICAMM (G-SICAMM). The generalization allows for flexible estimation of complex data densities, subspace classification, blind source separation, and accurate modeling of both local and global dynamic interactions. In this work, the structured result obtained by G-SICAMM was used in two ways: classification and interpretation. Classification performance was tested on an extensive number of simulations and a set of real electroencephalograms (EEG) from epileptic patients performing neuropsychological tests. G-SICAMM outperformed the following competitive methods: Gaussian mixture models, HMM, Coupled HMM, ICAMM, SICAMM, and a long short-term memory (LSTM) recurrent neural network. As for interpretation, the structured result returned by G-SICAMM on EEGs was mapped back onto the scalp, providing a set of brain activations. These activations were consistent with the physiological areas activated during the tests, thus proving the ability of the method to deal with different kind of data densities and changing non-stationary and non-linear brain dynamics. (C) 2019 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship This work was supported by Spanish Administration (Ministerio de Economia y Competitividad) and European Union (FEDER) under grants TEC2014-58438-R and TEC2017-84743-P. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation MINECO/TEC2017-84743-P es_ES
dc.relation info:eu-repo/grantAgreement/MINECO//TEC2014-58438-R/ES/PROCESADO DE SEÑAL SOBRE GRAFOS PARA SISTEMAS CLASIFICADORES: APLICACION EN SALUD, ENERGIA Y SEGURIDAD/ es_ES
dc.relation.ispartof Pattern Recognition es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Dynamic modeling es_ES
dc.subject Non-Gaussian mixtures es_ES
dc.subject ICA es_ES
dc.subject HMM es_ES
dc.subject EEG es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Multichannel dynamic modeling of non-Gaussian mixtures es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.patcog.2019.04.022 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L.; Gomez, E.; Villanueva, V. (2019). Multichannel dynamic modeling of non-Gaussian mixtures. Pattern Recognition. 93:312-323. https://doi.org/10.1016/j.patcog.2019.04.022 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.patcog.2019.04.022 es_ES
dc.description.upvformatpinicio 312 es_ES
dc.description.upvformatpfin 323 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 93 es_ES
dc.relation.pasarela S\408019 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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