Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity

Frontiers in Neuroscience

Rolando B. Lirio¹  Lídice Galán-García²  Milene Roca-Stappung³  Jorge Bosch-Bayard³  Thalia Fernandez³  Thalía Harmony³  Josefina Ricardo-Garcell³  Maria L. Bringas-Vega⁴  Pedro A. Valdes-Sosa⁴ 

[1] Centro de Investigación en Matemáticas, Guanajuato, Mexico;Cuban Neuroscience Center, La Habana, Cuba;Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico;The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education, Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China;

关键词: LD-NOS classification;    EEG classification;    stability based biomarkers;    non-parametric ROC;    sparse classifiers;    elastic-net;   

DOI  :  10.3389/fnins.2017.00749

来源: DOAJ

【 摘 要 】

In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

【 授权许可】

Unknown