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Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics

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Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics

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Cuesta Frau, D.; Miró Martínez, P.; Jordán Núñez, J.; Oltra Crespo, S.; Molina Picó, A. (2017). Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Computers in Biology and Medicine. 87:141-151. doi:10.1016/j.compbiomed.2017.05.028

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/102322

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Title: Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Issued date:
Embargo end date: 2018-08-01
Abstract:
[EN] This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution ...[+]
Subjects: Electroencephalograms , Signal Classification , Approximate Entropy , Sample Entropy , Fuzzy Entropy , EEG Artifacts
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Computers in Biology and Medicine. (issn: 0010-4825 )
DOI: 10.1016/j.compbiomed.2017.05.028
Publisher:
Elsevier
Publisher version: http://doi.org/10.1016/j.compbiomed.2017.05.028
Type: Artículo

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