RT info:eu-repo/semantics/article
T1 Data Augmentation Effects on Highly Imbalanced EEG Datasets for Automatic Detection of Photoparoxysmal Responses
A1 Moncada Martins, Fernando
A1 González Suárez, Víctor Manuel
A1 Villar Flecha, José Ramón
A1 García López, Beatriz
K1 Electroencephalography
K1 EEG
K1 Photoparoxysmal response
K1 PPR
K1 Machine Learning
K1 Data Augmentation
K1 Photosensitivity
K1 Epilepsy
K1 Neurología
K1 Neurology
K1 Fisiología
K1 Physiology
K1 Salud
K1 Health
AB 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.
PB MDPI
SN 1424-8220
YR 2023
FD 2023
LK http://hdl.handle.net/10259/8568
UL http://hdl.handle.net/10259/8568
LA eng
NO This research was funded by the Spanish Ministry of Economics and Industry, Grant PID2020-112726RB-I00, by the Spanish Research Agency (AEI, Spain) under Grant agreement RED2018-102312-T (IA-Biomed), and by the Ministry of Science and Innovation under CERVERA Excellence Network project CER-20211003 (IBERUS) and Missions Science and Innovation project MIG-20211008 (INMERBOT), as well as by Principado de Asturias, Grant SV-PA-21-AYUD/2021/50994.
DS Repositorio Institucional de la Universidad de Burgos
RD 11-dic-2024