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Automated detection of presymptomatic conditions in spinocerebellar ataxia type 2 using monte carlo dropout and deep neural network techniques with electrooculogram signals

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journal contribution
posted on 2024-06-18, 21:05 authored by C Stoean, R Stoean, M Atencia, M Abdar, L Velázquez-Pérez, Abbas KhosraviAbbas Khosravi, S Nahavandi, U Rajendra Acharya, G Joya
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.

History

Journal

Sensors (Switzerland)

Volume

20

Article number

ARTN 3032

Pagination

1 - 16

Location

Switzerland

Open access

  • Yes

ISSN

1424-8220

eISSN

1424-8220

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

11

Publisher

MDPI