Abstract
This 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 one-dimensional (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.
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Acknowledgments
The authors wish to thank the NeuroMorpho.Org project and Dr. Giorgio Ascoli for providing the information on traced neurons as well as statistical data used in this research for comparison purposes. We also acknowledge Drs. Carlos Morell-Pérez and María M. García-Lorenzo and BSc. José D. López-Cabrera for their useful comments and having providing useful references, as well as to the anonymous reviewers for their criticism and valuable comments that allowed us to introduce many improvements in the article.
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Hernández-Pérez, L.A., Delgado-Castillo, D., Martín-Pérez, R. et al. New Features for Neuron Classification. Neuroinform 17, 5–25 (2019). https://doi.org/10.1007/s12021-018-9374-0
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DOI: https://doi.org/10.1007/s12021-018-9374-0