New features for neuron classification

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dc.contributor.authorHernández Pérez, Leonardo Agustin
dc.contributor.authorDelgado Castillo, Duniel
dc.contributor.authorMartín Pérez, Rainer
dc.contributor.authorOrozco Morales, Rubén
dc.contributor.authorLorenzo Ginori, Juan Valentín
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Dpto de Automáticaen_US
dc.contributor.otherEmpresa de Telecomunicaciones de Cuba S.A, Santa Clara, Villa Clara, Cubaen_US
dc.date.accessioned2022-02-17T16:03:54Z
dc.date.available2022-02-17T16:03:54Z
dc.date.issued2018-04-28
dc.description.abstractThis paper addresses the problem of obtaining new neuron features capable of improving results of neuron classification. Most studies on neuron classification using morphological features have been based on Euclidean geometry. Here three onedimensional (1D) time series are derived from the three-dimensional (3D) structure of neuron instead, and afterwards a spatial time series is finally constructed from which the features are calculated. Digitally reconstructed neurons were separated into control and pathological sets, which are related to three categories of alterations caused by epilepsy, Alzheimer’s disease (long and local projections), and ischemia. These neuron sets were then subjected to supervised classification and the results were compared considering three sets of features: morphological, features obtained from the time series and a combination of both. The best results were obtained using features from the time series, which outperformed the classification using only morphological features, showing higher correct classification rates with differences of 5.15, 3.75, 5.33% for epilepsy and Alzheimer’s disease (long and local projections) respectively. The morphological features were better for the ischemia set with a difference of 3.05%. Features like variance, Spearman auto-correlation, partial auto-correlation, mutual information, local minima and maxima, all related to the time series, exhibited the best performance. Also we compared different evaluators, among which ReliefF was the best ranked.en_US
dc.identifier.doi10.1007/s12021-018-9374-0en_US
dc.identifier.urihttps://dspace.uclv.edu.cu/handle/123456789/13435
dc.language.isoen_USen_US
dc.relation.journalNeuroinformaticsen_US
dc.source.endpage25en_US
dc.source.initialpage5en_US
dc.source.issue1en_US
dc.source.volume17en_US
dc.subjectNeuron classificationen_US
dc.subjectReconstructed neuron treeen_US
dc.subjectNeuron featuresen_US
dc.titleNew features for neuron classificationen_US
dc.typeArticleen_US
dc.type.article1en_US

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