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.