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A fully automated method for segmentation and classification of local field potential recordings. Preliminary results

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A fully automated method for segmentation and classification of local field potential recordings. Preliminary results

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dc.contributor.author Díaz-Parra, Antonio es_ES
dc.contributor.author Canals, S. es_ES
dc.contributor.author Moratal, David es_ES
dc.date.accessioned 2020-07-23T03:31:21Z
dc.date.available 2020-07-23T03:31:21Z
dc.date.issued 2017-09-14 es_ES
dc.identifier.issn 1557-170X es_ES
dc.identifier.uri http://hdl.handle.net/10251/148522
dc.description.abstract [EN] Identification of brain states measured with electrophysiological methods such as electroencephalography and local field potential (LFP) recordings is of great importance in numerous neuroscientific applications. For instance, in Brain Computer Interface, in the diagnosis of neurological disorders as well as to investigate how brain rhythms stem from synchronized physiological mechanisms (e.g., memory and learning). In this work, we propose a fully automated method with the aim of partitioning LFP signals into stationary segments as well as classifying each detected segment into three different classes (delta, regular theta or irregular theta rhythms). Our approach is computationally efficient since the process of detection and partition of signals into stationary segments is only based on two features (the variance and the so-called spectral error measure) and allow the classification at the same time. We developed the algorithm upon analyzing six anesthetized rats, resulting in a true positive rate of 97.5%, 91.8% and 79.1% in detecting delta, irregular theta and regular theta rhythms, respectively. This preliminary quantitative evaluation offers encouraging results for further research. es_ES
dc.description.sponsorship Research supported by the Spanish Ministerio de Economia y Competitividad (MINECO) under grants BFU2015-64380-C2-1-R and BFU2015-64380-C2-2R and European Union's Horizon 2020 research and innovation programme under grant agreement No 668863 (SyBil-AA). Santiago Canals acknowledges financial support from the Spanish State Research Agency, through the "Severo Ochoa" Programme for Centres of Excellence in R&D (ref. SEV- 2013-0317). Antonio Diaz-Parra is funded by the Spanish Ministerio de Educacion, Cultura y Deporte (MECD) under grant FPU13/01475. es_ES
dc.language Inglés es_ES
dc.publisher IEEE Engineering in Medicine and Biology Society es_ES
dc.relation.ispartof Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society es_ES
dc.relation.ispartof 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Signals es_ES
dc.subject EEG es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title A fully automated method for segmentation and classification of local field potential recordings. Preliminary results es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1109/EMBC.2017.8036853 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/668863/EU/Systems Biology of Alcohol Addiction: Modeling and validating disease state networks in human and animal brains for understanding pathophysiolgy, predicting outcomes and improving therapy/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//BFU2015-64380-C2-1-R/ES/TRATAR LA ENFERMEDAD RESINTONIZANDO LA DINAMICA DE LAS REDES CEREBRALES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//SEV-2013-0317/ES/-/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//BFU2015-64380-C2-2-R/ES/ANALISIS DE TEXTURAS EN IMAGEN CEREBRAL MULTIMODAL POR RESONANCIA MAGNETICA PARA UNA DETECCION TEMPRANA DE ALTERACIONES EN LA RED Y BIOMARCADORES DE ENFERMEDAD/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU13%2F01475/ES/FPU13%2F01475/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Díaz-Parra, A.; Canals, S.; Moratal, D. (2017). A fully automated method for segmentation and classification of local field potential recordings. Preliminary results. Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society. 426-429. https://doi.org/10.1109/EMBC.2017.8036853 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017) es_ES
dc.relation.conferencedate Julio 11-15,2017 es_ES
dc.relation.conferenceplace Jeju Island, South Korea es_ES
dc.relation.publisherversion https://doi.org/10.1109/EMBC.2017.8036853 es_ES
dc.description.upvformatpinicio 426 es_ES
dc.description.upvformatpfin 429 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.identifier.pmid 29059901 es_ES
dc.relation.pasarela S\353077 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Ministerio de Educación, Cultura y Deporte es_ES


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