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Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13258)

Abstract

The search for a dyslexia diagnosis based on exclusively objective methods is currently a challenging task. Usually, this disorder is analyzed by means of behavioral tests prone to errors due to their subjective nature; e.g. the subject’s mood while doing the test can affect the results. Understanding the brain processes involved is key to proportionate a correct analysis and avoid these types of problems. It is in this task, biomarkers like electroencephalograms can help to obtain an objective measurement of the brain behavior that can be used to perform several analyses and ultimately making a diagnosis, keeping the human interaction at minimum. In this work, we used recorded electroencephalograms of children with and without dyslexia while a sound stimulus is played. We aim to detect whether there are significant differences in adaptation when the same stimulus is applied at different times. Our results show that following this process, a machine learning pipeline can be built with AUC values up to 0.73.

Keywords

  • EEG
  • Hilber transform
  • Dyslexia
  • Neural adaptation

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Notes

  1. 1.

    The study was carried out with the understanding and written consent of each child’s legal guardian and in the presence thereof, and was approved by the Medical Ethical Committee of the Malaga University (ref. 16-2020-H) and according to the dispositions of the World Medical Association Declaration of Helsinki.

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Acknowledgments

This work was supported by projects PGC2018-098813-B-C32, PGC2018-098813-B-C31 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086 and P18-RT-1624 (Consejería de economía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF) as well as the BioSiP (TIC-251) research group. M. A. Formoso Grant PRE2019-087350 funded by MCIN/AEI/ 10.13039/501100011033 by “ESF Investing in your future”. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Incorporación” Fellowship. We also thank the Leeduca research group and Junta de Andalucía for the data supplied and the support. Funding for open access charge: Spanish “Ministerio de Ciencia, Innovación y Universidades”, and European Regional Development Funds (ERDF).

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Formoso, M.A., Ortiz, A., Martínez-Murcia, F.J., Brítez, D.A., Escobar, J.J., Luque, J.L. (2022). Temporal Phase Synchrony Disruption in Dyslexia: Anomaly Patterns in Auditory Processing. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_2

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_2

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