2026-04-19T18:18:15Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/85682024-02-03T01:05:42Zcom_10259_7328com_10259_3989com_10259.4_106com_10259_2604col_10259_7329

00925njm 22002777a 4500 dc Moncada Martins, Fernando author González Suárez, Víctor Manuel author Villar Flecha, José Ramón author García López, Beatriz author 2023 Photosensitivity is a neurological disorder in which a person’s brain produces epileptic discharges, known as Photoparoxysmal Responses (PPRs), when it receives certain visual stimuli. The current standardized diagnosis process used in hospitals consists of submitting the subject to the Intermittent Photic Stimulation process and attempting to trigger these phenomena. The brain activity is measured by an Electroencephalogram (EEG), and the clinical specialists manually look for the PPRs that were provoked during the session. Due to the nature of this disorder, long EEG recordings may contain very few PPR segments, meaning that a highly imbalanced dataset is available. To tackle this problem, this research focused on applying Data Augmentation (DA) to create synthetic PPR segments from the real ones, improving the balance of the dataset and, thus, the global performance of the Machine Learning techniques applied for automatic PPR detection. K-Nearest Neighbors and a One-Hidden-Dense-Layer Neural Network were employed to evaluate the performance of this DA stage. The results showed that DA is able to improve the models, making them more robust and more able to generalize. A comparison with the results obtained from a previous experiment also showed a performance improvement of around 20% for the Accuracy and Specificity measurements without Sensitivity suffering any losses. This project is currently being carried out with subjects at Burgos University Hospital, Spain. 1424-8220 http://hdl.handle.net/10259/8568 10.3390/s23042312 1424-8220 Electroencephalography EEG Photoparoxysmal response PPR Machine Learning Data Augmentation Photosensitivity Epilepsy Data Augmentation Effects on Highly Imbalanced EEG Datasets for Automatic Detection of Photoparoxysmal Responses