Characterising an ECG signal using statistical modelling: a feasibility study

Bodisco, Timothy, D'Netto, J, Kelson, N, Banks, J, Hayward, R and Parker, T 2014, Characterising an ECG signal using statistical modelling: a feasibility study, Australia and New Zealand industrial and applied mathematics (ANZIAM) journal, vol. 55, pp. C32-C46, doi: 10.0000/anziamj.v55i0.7818.

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Title Characterising an ECG signal using statistical modelling: a feasibility study
Author(s) Bodisco, TimothyORCID iD for Bodisco, Timothy orcid.org/0000-0002-5163-4762
D'Netto, J
Kelson, N
Banks, J
Hayward, R
Parker, T
Journal name Australia and New Zealand industrial and applied mathematics (ANZIAM) journal
Volume number 55
Start page C32
End page C46
Total pages 15
Publisher Australian Mathematical Society
Place of publication Canberra, A.C.T
Publication date 2014-03-26
ISSN 1446-8735
Keyword(s) ECG
Statistical Modelling
MCMC
Summary 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.
Language eng
DOI 10.0000/anziamj.v55i0.7818
Field of Research 090599 Civil Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Australian Mathematical Society
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082247

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