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

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

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 therefor...

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Bibliographic Details
Main Authors: Catalin Stoean, Ruxandra Stoean, Miguel Atencia, Moloud Abdar, Luis Velázquez-Pérez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya, Gonzalo Joya
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Sensors
Subjects:
deep learning
medicine
sensor data
electrooculogram
uncertainty quantification
Monte Carlo dropout
Online Access:https://www.mdpi.com/1424-8220/20/11/3032

Internet

https://www.mdpi.com/1424-8220/20/11/3032

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