Output details
11 - Computer Science and Informatics
Aston University
Article title
Classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome
Type
D - Journal article
Title of journal
Physiological measurement
Article number
-
Volume number
31
Issue number
3
First page of article
375
ISSN of journal
0967-3334
Year of publication
2010
Number of additional authors
5
Additional information
<24> This paper provides a statistically rigorous analysis of the improvements in classification performance achieved with Bayesian machine-learning models rather than maximum likelihood or the conventional clinical indices for a complex medical diagnostic task. It was the result of an international collaboration with the University of Valladolid, Spain.
Interdisciplinary
-
Cross-referral requested
-
Research group
A - Nonlinearity and Complexity Research Group
Citation count
4
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-