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
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
Resumen
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
Versión del editor
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