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Updated: Jul 3, 2026
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry
K C Chua1, V Chandran, U R Acharya
1Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore. ckc@np.edu.sg
Insights
Heart rate variability analysis using computational methods can accurately detect cardiac abnormalities. Approximate entropy (ApEn) demonstrated superior performance in identifying various heart conditions with over 95% accuracy.
Area of Science:
- Cardiology
- Biomedical Engineering
- Computational Medicine
Background:
- Heart rate variability (HRV) reflects autonomic nervous system regulation of the sinoatrial node.
- HRV analysis offers insights into the heart's dynamic response to regulatory impulses.
- Computer-based systems are crucial for analyzing cardiac states in diagnostics and disease management.
Purpose of the Study:
- To analyze normal and seven types of cardiac abnormal heart rate signals.
- To evaluate the effectiveness of approximate entropy (ApEn), sample entropy (SampEn), recurrence plots, and Poincare plots in cardiac diagnostics.
- To establish parameter ranges for various cardiac abnormalities.
Main Methods:
- Extraction and computer-aided analysis of parameters from heart rate signals.
- Application of approximate entropy (ApEn) and sample entropy (SampEn) for quantifying signal complexity.
- Utilizing recurrence plots and Poincare plots for pattern recognition of cardiac states.
Main Results:
- Parameter ranges for normal and abnormal cardiac conditions were determined.
- An overall diagnostic accuracy exceeding 95% was achieved.
- Approximate entropy (ApEn) exhibited superior performance across all analyzed cardiac abnormalities compared to sample entropy (SampEn).
Conclusions:
- Computational analysis of HRV parameters, particularly ApEn, provides a highly accurate method for diagnosing cardiac abnormalities.
- The study establishes valuable reference ranges for HRV parameters in various cardiac conditions.
- The findings support the utility of intelligent systems for cardiac diagnostics and disease management.
Abstract:
Heart rate variability refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability is important because it provides a window to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Parameters are extracted from the heart rate signals and analysed using computers for diagnostics. This paper describes the analysis of normal and seven types of cardiac abnormal signals using approximate entropy (ApEn), sample entropy (SampEn), recurrence plots and Poincare plot patterns. Ranges of these parameters for various cardiac abnormalities are presented with an accuracy of more than 95%. Among the two entropies, ApEn showed better performance for all the cardiac abnormalities. Typical Poincare and recurrence plots are shown for various cardiac abnormalities.
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