Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis

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 i...

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Autores principales:	Marco A. Formoso, Andrés Ortiz, Francisco J. Martinez-Murcia, Nicolás Gallego, Juan L. Luque
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	functional connectivity EEG anomaly detection self-organizing maps phase locking value circular correlation Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/2312058bb83340f0b9fdc135188d57e0
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id	oai:doaj.org-article:2312058bb83340f0b9fdc135188d57e0
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spelling	oai:doaj.org-article:2312058bb83340f0b9fdc135188d57e02021-11-11T19:05:26ZDetecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis10.3390/s212170611424-8220https://doaj.org/article/2312058bb83340f0b9fdc135188d57e02021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7061https://doaj.org/toc/1424-8220Objective 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.Marco A. FormosoAndrés OrtizFrancisco J. Martinez-MurciaNicolás GallegoJuan L. LuqueMDPI AGarticlefunctional connectivityEEGanomaly detectionself-organizing mapsphase locking valuecircular correlationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7061, p 7061 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	functional connectivity EEG anomaly detection self-organizing maps phase locking value circular correlation Chemical technology TP1-1185
spellingShingle	functional connectivity EEG anomaly detection self-organizing maps phase locking value circular correlation Chemical technology TP1-1185 Marco A. Formoso Andrés Ortiz Francisco J. Martinez-Murcia Nicolás Gallego Juan L. Luque Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis
description	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.
format	article
author	Marco A. Formoso Andrés Ortiz Francisco J. Martinez-Murcia Nicolás Gallego Juan L. Luque
author_facet	Marco A. Formoso Andrés Ortiz Francisco J. Martinez-Murcia Nicolás Gallego Juan L. Luque
author_sort	Marco A. Formoso
title	Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis
title_short	Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis
title_full	Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis
title_fullStr	Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis
title_full_unstemmed	Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis
title_sort	detecting phase-synchrony connectivity anomalies in eeg signals. application to dyslexia diagnosis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2312058bb83340f0b9fdc135188d57e0
work_keys_str_mv	AT marcoaformoso detectingphasesynchronyconnectivityanomaliesineegsignalsapplicationtodyslexiadiagnosis AT andresortiz detectingphasesynchronyconnectivityanomaliesineegsignalsapplicationtodyslexiadiagnosis AT franciscojmartinezmurcia detectingphasesynchronyconnectivityanomaliesineegsignalsapplicationtodyslexiadiagnosis AT nicolasgallego detectingphasesynchronyconnectivityanomaliesineegsignalsapplicationtodyslexiadiagnosis AT juanlluque detectingphasesynchronyconnectivityanomaliesineegsignalsapplicationtodyslexiadiagnosis
_version_	1718431631925051392

Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis

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