You are not logged in.

Classification of implantable rotary blood pump states with class noise

Ooi, Hui-Lee, Seera, Manjeevan, Ng, Siew-Cheok, Lim, Chee Peng, Loo, Chu Kiong, Lovell, Nigel H., Redmond, Stephen J. and Lim, Einly 2016, Classification of implantable rotary blood pump states with class noise, IEEE journal of biomedical and health informatics, vol. 20, no. 3, pp. 829-837, doi: 10.1109/JBHI.2015.2412375.

Attached Files
Name Description MIMEType Size Downloads

Title Classification of implantable rotary blood pump states with class noise
Author(s) Ooi, Hui-Lee
Seera, Manjeevan
Ng, Siew-Cheok
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Loo, Chu Kiong
Lovell, Nigel H.
Redmond, Stephen J.
Lim, Einly
Journal name IEEE journal of biomedical and health informatics
Volume number 20
Issue number 3
Start page 829
End page 837
Total pages 9
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2016-05
ISSN 2168-2208
Keyword(s) noise
blood
valves
heart
informatics
training
robustness
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Medical Informatics
Computer Science
Classification
classifier
class noise
ensemble classifier
implantable rotary blood pump (IRBP)
left ventricular assist device (VAD)
mislabeling
pump state classification
VENTRICULAR ASSIST DEVICE
SUCTION DETECTION
LOGISTIC-REGRESSION
CONTROL-SYSTEM
HEART-FAILURE
FLOW PUMP
SUPPORT
SELECTION
ALGORITHM
OPTIMIZATION
Summary A medical case study related to implantable rotary blood pumps is examined. Five classifiers and two ensemble classifiers are applied to process the signals collected from the pumps for the identification of the aortic valve nonopening pump state. In addition to the noise-free datasets, up to 40% class noise has been added to the signals to evaluate the classification performance when mislabeling is present in the classifier training set. In order to ensure a reliable diagnostic model for the identification of the pump states, classifications performed with and without class noise are evaluated. The multilayer perceptron emerged as the best performing classifier for pump state detection due to its high accuracy as well as robustness against class noise.
Language eng
DOI 10.1109/JBHI.2015.2412375
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084923

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 20 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Fri, 15 Jul 2016, 10:37:54 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.