Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals
Stoean, C.; Stoean, R.; Atencia, M.; Abdar, M.; Velázquez-Pérez, L.; Khosravi, A.; Nahavandi, S.; Acharya, U.R.; Joya, G. Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals. Sensors 2020, 20, 3032. https://doi.org/10.3390/s20113032
Stoean C, Stoean R, Atencia M, Abdar M, Velázquez-Pérez L, Khosravi A, Nahavandi S, Acharya UR, Joya G. Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals. Sensors. 2020; 20(11):3032. https://doi.org/10.3390/s20113032
Chicago/Turabian StyleStoean, Catalin, Ruxandra Stoean, Miguel Atencia, Moloud Abdar, Luis Velázquez-Pérez, Abbas Khosravi, Saeid Nahavandi, U. R. Acharya, and Gonzalo Joya. 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, no. 11: 3032. https://doi.org/10.3390/s20113032