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Analysis of Major Adverse Cardiac Events with Entropy-Based Complexity
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia .ORCID iD: 0000-0002-4255-5130
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.
Clinical Center, National Institutes of Health, Bethesda, USA.
Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, USA.
2010 (English)In: Information Technologies in Biomedicine: Volume 2 / [ed] Ewa Pietka and Jacek Kawa, Springer Berlin/Heidelberg, 2010, 261-272 p.Chapter in book (Refereed)Text
Abstract [en]

Major adverse cardiac events (MACE) are referred to as unsuspected heart attacks that include death, myocardial infarction and target lesion revascularization. Feature extraction and classification methods for such cardiac events are useful tools that can be applied for biomarker discovery to allow preventive treatment and healthy-life maintenance. In this study we present an entropy-based analysis of the complexity of MACE-related mass spectrometry signals, and an effective model for classifying MACE and control complexity-based features. In particular, the geostatistical entropy is analytically rigorous and can provide better information about the predictability of this type of MACE data than other entropy-based methods for complexity analysis of biosignals. Information on the complexity of this type of time-series data can expand our knowledge about the dynamical behavior of a cardiac model and be useful as a novel feature for early prediction.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2010. 261-272 p.
, Advances in Intelligent and Soft Computing, ISSN 1867-5662 ; 69
National Category
Bioinformatics (Computational Biology)
URN: urn:nbn:se:liu:diva-125029DOI: 10.1007/978-3-642-13105-9_27ISBN: 978-3-642-13104-2 (Print)ISBN: 978-3-642-13105-9 (Online)OAI: diva2:902764
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-24Bibliographically approved

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Pham, Tuan D
Bioinformatics (Computational Biology)

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ReferencesLink to record
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