Openly accessible

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, Moloud, Velázquez-Pérez, L., Khosravi, Abbas, Nahavandi, Saeid, Rajendra Acharya, U. and 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, vol. 20, no. 11, pp. 1-16, doi: 10.3390/s20113032.

Attached Files
Name Description MIMEType Size Downloads

Title Automated detection of presymptomatic conditions in spinocerebellar ataxia type 2 using monte carlo dropout and deep neural network techniques with electrooculogram signals
Author(s) Stoean, C.
Stoean, R.
Atencia, M.
Abdar, MoloudORCID iD for Abdar, Moloud orcid.org/0000-0002-3059-6357
Velázquez-Pérez, L.
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Rajendra Acharya, U.
Joya, G.
Journal name Sensors
Volume number 20
Issue number 11
Article ID 3032
Start page 1
End page 16
Total pages 16
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020
ISSN 1424-8220
1424-8220
Keyword(s) Science & Technology
Physical Sciences
Technology
Chemistry, Analytical
Engineering, Electrical & Electronic
Instruments & Instrumentation
Chemistry
Engineering
deep learning
medicine
sensor data
electrooculogram
uncertainty quantification
Monte Carlo dropout
decision trees
IDENTIFICATION
DIAGNOSIS
Summary 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.
Language eng
DOI 10.3390/s20113032
Indigenous content off
Field of Research 0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
0502 Environmental Science and Management
0602 Ecology
HERDC Research category C1.1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30138861

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in TR Web of Science
Scopus Citation Count Cited 2 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 37 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Fri, 12 Jun 2020, 14:20:00 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.