Fast statistical model-based classification of epileptic EEG signals

Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Risk, Marcelo; Batatia, Hadj

artículo de publicación periódica.page.titleprefix
Fast statistical model-based classification of epileptic EEG signals

dc.contributor.author	Quintero-Rincón, Antonio
dc.contributor.author	Pereyra, Marcelo
dc.contributor.author	D'Giano, Carlos
dc.contributor.author	Risk, Marcelo
dc.contributor.author	Batatia, Hadj
dc.date.accessioned	2019-06-24T17:59:26Z
dc.date.available	2019-06-24T17:59:26Z
dc.date.issued	2018-01
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.identifier.issn	0208-5216
dc.identifier.uri	http://ri.itba.edu.ar/handle/123456789/1633
dc.language.iso	en	en
dc.relation	info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bbe.2018.08.002
dc.relation	info:eu-repo/grantAgreement/EPSRC/EP/D063485/1/UK. Swindon
dc.relation	info:eu-repo/grantAgreement/ITBACyT/34/2015/AR. Ciudad Autónoma de Buenos Aires
dc.relation	info:eu-repo/grantAgreement/FLENI/Protocolo/07/15/AR. Ciudad de Buenos Aires
dc.relation	info:eu-repo/grantAgreement/ANID/STICAmSUD/CL. Santiago/DynBrain
dc.subject	EPILEPSIA	es
dc.subject	ELECTROENCEFALOGRAFIA	es
dc.subject	PROCESAMIENTO DE SEÑALES	es
dc.subject	ESTADISTICA	es
dc.subject	ALGORITMOS	es
dc.title	Fast statistical model-based classification of epileptic EEG signals	en
dc.type	Artículos de Publicaciones Periódicas	es
dc.type	info:eu-repo/semantics/acceptedVersion
dspace.entity.type	Artículo de Publicación Periódica
itba.description.filiation	Fil: Quintero-Rincón, Antonio. Instituto Tecnológico de Buenos Aires; Argentina
itba.description.filiation	Fil: Pereyra, Marcelo. Heriot-Watt University; Escocia.
itba.description.filiation	Fil: D'Giano, Carlos. Fundación para la Lucha contra las Enfermedades Neurológicas Infantiles; Argentina.
itba.description.filiation	Fil: Risk, Marcelo. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiation	Fil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
itba.description.filiation	Fil: Batatia, Hadj. Université Fédérale Toulouse Midi-Pyrénées; Francia.