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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/8567

    Título
    Virtual reality and machine learning in the automatic photoparoxysmal response detection
    Autor
    Moncada, Fernando
    Martín, Sofía
    González, Víctor M.
    Álvarez, Víctor M.
    García López, BeatrizUBU authority Orcid
    Gómez Menéndez, Ana Isabel
    Villar, José R.
    Publicado en
    Neural Computing and Applications. 2022, V. 35, n. 8, p. 5643-5659
    Editorial
    Springer
    Fecha de publicación
    2022
    ISSN
    0941-0643
    DOI
    10.1007/S00521-022-06940-Z
    Abstract
    Photosensitivity, in relation to epilepsy, is a genetically determined condition in which patients have epileptic seizures of different severity provoked by visual stimuli. It can be diagnosed by detecting epileptiform discharges in their electroencephalogram (EEG), known as photoparoxysmal responses (PPR). The most accepted PPR detection method—a manual method—considered as the standard one, consists in submitting the subject to intermittent photic stimulation (IPS), i.e. a flashing light stimulation at increasing and decreasing flickering frequencies in a hospital room under controlled ambient conditions, while at the same time recording her/his brain response by means of EEG signals. This research focuses on introducing virtual reality (VR) in this context, adding, to the conventional infrastructure a more flexible one that can be programmed and that will allow developing a much wider and richer set of experiments in order to detect neurological illnesses, and to study subjects’ behaviours automatically. The loop includes the subject, the VR device, the EEG infrastructure and a computer to analyse and monitor the EEG signal and, in some cases, provide feedback to the VR. As will be shown, AI modelling will be needed in the automatic detection of PPR, but it would also be used in extending the functionality of this system with more advanced features. This system is currently in study with subjects at Burgos University Hospital, Spain.
    Palabras clave
    Electroencefalogram
    Virtual reality
    Photoparoxysmal response
    Machine learning
    Materia
    Neurología
    Neurology
    Fisiología
    Physiology
    Salud
    Health
    URI
    http://hdl.handle.net/10259/8567
    Versión del editor
    https://doi.org/10.1007/S00521-022-06940-Z
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