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Open AccessArticle

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals

1
Romanian Institute of Science and Technology, 400022 Cluj-Napoca, Romania
2
Department of Applied Mathematics, Universidad de Málaga, 29071 Málaga, Spain
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Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong 3216, Australia
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Cuban Academy of Sciences, La Habana 10100, Cuba
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Center for Research and Rehabilitation of Hereditary Ataxias, Holguín 80100, Cuba
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Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore
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Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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Department of Electronic Technology, Universidad de Málaga, 29071 Málaga, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3032; https://doi.org/10.3390/s20113032
Received: 21 April 2020 / Revised: 25 May 2020 / Accepted: 25 May 2020 / Published: 27 May 2020
(This article belongs to the Special Issue Artificial Intelligence for Mobile Health)
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. View Full-Text
Keywords: deep learning; medicine; sensor data; electrooculogram; uncertainty quantification; Monte Carlo dropout; decision trees deep learning; medicine; sensor data; electrooculogram; uncertainty quantification; Monte Carlo dropout; decision trees
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MDPI and ACS Style

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

AMA Style

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 Style

Stoean, Catalin; Stoean, Ruxandra; Atencia, Miguel; Abdar, Moloud; Velázquez-Pérez, Luis; Khosravi, Abbas; Nahavandi, Saeid; Acharya, U. R.; Joya, Gonzalo. 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

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