@misc{10481/71382,
year = {2021},
url = {http://hdl.handle.net/10481/71382},
abstract = {Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are
not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been
addressed by these tests in clinical practice, it is difficult to extract information about the brain
processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use
of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific
learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals
to discover differences among controls and dyslexic subjects using signal processing and artificial
intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal
the functional brain network activated during auditory processing. On the other hand, to explore
synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting
in band-limited white noise, modulated in amplitude at different frequencies. The differential
information contained in the functional (i.e., synchronization) network has been processed by an
anomaly detection system that addresses the problem of subjects variability by an outlier-detection
method based on vector quantization. The results, obtained for 7 years-old children, show that the
proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC)
values up to 0.95 in differential diagnosis tasks.},
organization = {PGC2018-098813-B-C32 (Spanish “Ministerio de
Ciencia, Innovación y Universidades”)},
organization = {UMA20-FEDERJA-086 (Consejería de econnomía y
conocimiento, Junta de Andalucía)},
organization = {European Regional Development Funds (ERDF)},
organization = {Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing
in your future”.},
publisher = {MDPI},
keywords = {Functional connectivity},
keywords = {EEG},
keywords = {Anomaly detection},
keywords = {Self-organizing map},
keywords = {Phase locking value},
keywords = {Circular correlation},
title = {Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis},
doi = {10.3390/s21217061},
author = {Formoso, Marco A. and Ortiz, Andrés and Martínez Murcia, Francisco Jesús and Gallego, Nicolás and Luque, Juan L.},
}