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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7061 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:52:39Z |
| format | Article |
| id | doaj.art-2312058bb83340f0b9fdc135188d57e0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:39Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2312058bb83340f0b9fdc135188d57e02023-11-22T21:36:10ZengMDPI AGSensors1424-82202021-10-012121706110.3390/s21217061Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia DiagnosisMarco A. Formoso0Andrés Ortiz1Francisco J. Martinez-Murcia2Nicolás Gallego3Juan L. Luque4Communications Engineering Department, University of Málaga, 29071 Málaga, SpainCommunications Engineering Department, University of Málaga, 29071 Málaga, SpainAndalusian Research Institute in Data Science and Computational Intelligence (DaSCI), 18014 Granada, SpainCommunications Engineering Department, University of Málaga, 29071 Málaga, SpainDepartment of Basic Psychology, University of Malaga, 29019 Málaga, SpainObjective 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.https://www.mdpi.com/1424-8220/21/21/7061functional connectivityEEGanomaly detectionself-organizing mapsphase locking valuecircular correlation |
| spellingShingle | 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 Sensors functional connectivity EEG anomaly detection self-organizing maps phase locking value circular correlation |
| title | 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_short | 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 |
| topic | functional connectivity EEG anomaly detection self-organizing maps phase locking value circular correlation |
| url | https://www.mdpi.com/1424-8220/21/21/7061 |
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