RT Journal Article
T1 Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals
A1 Stoean, Catalin
A1 Stoean, Ruxandra
A1 Atencia-Ruiz, Miguel Alejandro
A1 Abdar, Moulud
A1 Velázquez-Pérez, Luis
A1 Khosravi, Abbas
A1 Nahavandi, Saeid
A1 Acharya, U. Rajendra
A1 Joya-Caparrós, Gonzalo
K1 Electrofisiología
K1 Aprendizaje automático (Inteligencia artificial)
K1 Montecarlo, Método de
K1 Informática médica
AB Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.
PB MDPI
YR 2020
FD 2020-05-27
LK https://hdl.handle.net/10630/41429
UL https://hdl.handle.net/10630/41429
LA eng
NO Stoean, C., Stoean, R., Atencia, M., Abdar, M., Velázquez-Pérez, L., Khosravi, A., Nahavandi, S., Acharya, U. R., & Joya, G. (2020). Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals. Sensors, 20(11), 3032
NO Ministerio de Ciencia e Innovación. Plan Estatal de Investigación Científica y Técnica y de Innovación
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 ene 2026