Characterising an ECG signal using statistical modelling : a feasibility study
Data(s) |
26/03/2014
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Resumo |
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. |
Identificador | |
Publicador |
Australian Mathematical Society |
Relação |
http://journal.austms.org.au/ojs/index.php/ANZIAMJ/article/view/7818/1776 DOI:10.0000/anziamj.v55i0.7818 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. |
Direitos |
Copyright 2014 Australian Mathematical Society Copies of this article must not be made otherwise available on the internet; instead link directly to this url for this article. |
Fonte |
School of Chemistry, Physics & Mechanical Engineering; Division of Technology, Information and Learning Support; School of Electrical Engineering & Computer Science; Faculty of Health; High Performance Computing and Research Support; Institute of Health and Biomedical Innovation; Science & Engineering Faculty; School of Public Health & Social Work |
Palavras-Chave | #010401 Applied Statistics #090303 Biomedical Instrumentation #090609 Signal Processing #ECG #Statistical Modelling #MCMC |
Tipo |
Journal Article |