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dc.contributor.authorQuintero-Rincón, Antonio
dc.contributor.authorPereyra, Marcelo
dc.contributor.authorD'Giano, Carlos
dc.contributor.authorRisk, Marcelo
dc.contributor.authorBatatia, Hadj
dc.date.accessioned2019-06-24T17:59:26Z
dc.date.available2019-06-24T17:59:26Z
dc.date.issued2018-01
dc.identifier.issn0208-5216
dc.identifier.urihttp://ri.itba.edu.ar/handle/123456789/1633
dc.description.abstract"This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using awavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature."en
dc.language.isoenen
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.bbe.2018.08.002
dc.relationinfo:eu-repo/grantAgreement/EPSRC/EP/D063485/1/UK. Bristol
dc.relationinfo:eu-repo/grantAgreement/ITBACyT/34/2015/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/FLENI/07/15Protocol/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/STICAmSUD/DynBrain/
dc.subjectEPILEPSIAes
dc.subjectELECTROENCEFALOGRAFIAes
dc.subjectPROCESAMIENTO DE SEÑALESes
dc.subjectESTADISTICAes
dc.subjectALGORITMOSes
dc.titleFast statistical model-based classification of epileptic EEG signalsen
dc.typeArtículos de Publicaciones Periódicases
dc.typeinfo:eu-repo/semantics/acceptedVersion
itba.description.filiationFil: Quintero-Rincón, Antonio. Instituto Tecnológico de Buenos Aires; Argentinaes
itba.description.filiationFil: Pereyra, Marcelo. Heriot-Watt University; Escocia.es
itba.description.filiationFil: D'Giano, Carlos. Fundación Lucha contra las Enfermedades Neurológicas Infantiles; Argentina.es
itba.description.filiationFil: Risk, Marcelo. Instituto Tecnológico de Buenos Aires; Argentina.es
itba.description.filiationFil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.es
itba.description.filiationFil: Batatia, Hadj. University of Toulouse; Francia.es


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