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Information Technologies in Biomedicine

Volume 69 of the series Advances in Intelligent and Soft Computing pp 261-272

Analysis of Major Adverse Cardiac Events with Entropy-Based Complexity

  • Tuan D. PhamAffiliated withSchool of Engineering and Information Technology, University of New South Wales
  • , Cuong C. ToAffiliated withSchool of Engineering and Information Technology, University of New South Wales
  • , Honghui WangAffiliated withClinical Center, National Institutes of Health
  • , Xiaobo ZhouAffiliated withCenter for Biotechnology and Informatics, The Methodist Hospital Research Institute, Weill Cornell Medical College

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Abstract

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.