Characterising an ECG signal using statistical modelling : a feasibility study
Bodisco, Timothy A., D'Netto, Jason, Kelson, Neil A., Banks, Jasmine, Hayward, Ross F., & Parker, Tony W. (2014) Characterising an ECG signal using statistical modelling : a feasibility study. The ANZIAM Journal, 55, c32-c46.
For clinical use, in electrocardiogram (ECG) signal analysis it is important to detect not only the centre of the P wave, the QRS complex and the T wave, but also the time intervals, such as the ST segment. Much research focused entirely on qrs complex detection, via methods such as wavelet transforms, spline fitting and neural networks. However, drawbacks include the false classification of a severe noise spike as a QRS complex, possibly requiring manual editing, or the omission of information contained in other regions of the ECG signal. While some attempts were made to develop algorithms to detect additional signal characteristics, such as P and T waves, the reported success rates are subject to change from person-to-person and beat-to-beat. To address this variability we propose the use of Markov-chain Monte Carlo statistical modelling to extract the key features of an ECG signal and we report on a feasibility study to investigate the utility of the approach. The modelling approach is examined with reference to a realistic computer generated ECG signal, where details such as wave morphology and noise levels are variable.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
part of the Proceedings of the 11th Biennial Engineering Mathematics and Applications
|Keywords:||ECG, Statistical Modelling, MCMC|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > BIOMEDICAL ENGINEERING (090300) > Biomedical Instrumentation (090303)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
|Divisions:||Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Division of Technology, Information and Library Services
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Faculty of Health
Current > Research Centres > High Performance Computing and Research Support
Current > Institutes > Institute of Health and Biomedical Innovation
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Public Health & Social Work
|Copyright Owner:||Copyright 2014 Australian Mathematical Society|
|Copyright Statement:||Copies of this article
must not be made otherwise available on the internet; instead link directly to this url for
|Deposited On:||27 Mar 2014 03:50|
|Last Modified:||01 Apr 2014 02:31|
Repository Staff Only: item control page