2026-06-06T04:23:57Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/414292026-02-03T11:35:50Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Stoean, Catalin author Stoean, Ruxandra author Atencia-Ruiz, Miguel Alejandro author Abdar, Moulud author Velázquez-Pérez, Luis author Khosravi, Abbas author Nahavandi, Saeid author Acharya, U. Rajendra author Joya-Caparrós, Gonzalo author 2020-05-27 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. 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 https://hdl.handle.net/10630/41429 10.3390/s20113032 Electrofisiología Aprendizaje automático (Inteligencia artificial) Montecarlo, Método de Informática médica Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals