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

[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 from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) sig...

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Detalles Bibliográficos
Autores: Cuesta Frau, David|||0000-0002-0076-0515, Miró Martínez, Pau|||0000-0001-9573-9104, Jordán-Núñez, Jorge|||0000-0001-8178-9987, Oltra Crespo, Sandra|||0000-0003-1995-2557, Molina Picó, Antonio|||0000-0003-2414-5864
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/102322
Acceso en línea:https://riunet.upv.es/handle/10251/102322
Access Level:acceso abierto
Palabra clave:Electroencephalograms
Signal Classification
Approximate Entropy
Sample Entropy
Fuzzy Entropy
EEG Artifacts
MATEMATICA APLICADA
ESTADISTICA E INVESTIGACION OPERATIVA
ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES
Descripción
Sumario:[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 from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.

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