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Published on: August 9, 2024
Renyi Distribution Entropy Analysis of Short-Term Heart Rate Variability Signals and Its Application in Coronary
Manhong Shi1,2, Chaoying Zhan1, Hongxin He1
1Center for Systems Biology, Soochow University, Suzhou, China.
Insights
We developed Renyi Distribution Entropy (RdisEn) for early coronary artery disease (CAD) detection using heart rate variability (HRV). This novel method achieved 97.5% accuracy, outperforming existing techniques for reliable CAD identification.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Coronary artery disease (CAD) poses significant health risks, necessitating early detection through electrocardiography (ECG) signal analysis.
- Analyzing heart rate variability (HRV) is crucial for identifying diagnostic features of CAD.
Purpose of the Study:
- To introduce Renyi Distribution Entropy (RdisEn), a novel entropy measure for analyzing short-term HRV signals.
- To develop and validate a combined CAD detection scheme using RdisEn and wavelet packet decomposition (WPD).
Main Methods:
- RdisEn was proposed and evaluated for its stability and parameter independence in short-term HRV analysis.
- A CAD detection scheme was developed involving feature extraction (RdisEn and WPD) and classification (K-nearest neighbor, support vector machine).
- Clinically significant features were selected using student's t-test.
Main Results:
- RdisEn demonstrated effectiveness in distinguishing between different subject groups in simulation experiments.
- The combined RdisEn and WPD scheme achieved high accuracy (97.5%), sensitivity (100%), and specificity (95%) in CAD detection.
- The proposed method outperformed existing CAD detection schemes.
Conclusions:
- Renyi Distribution Entropy is a robust and effective tool for analyzing HRV signals, particularly for short-term data.
- The integrated CAD detection scheme combining RdisEn and WPD offers a highly accurate and reliable approach for early diagnosis.
- This novel method holds promise for improving the early detection and management of coronary artery disease.
Abstract:
Coronary artery disease (CAD) is a life-threatening condition that, unless treated at an early stage, can lead to congestive heart failure, ischemic heart disease, and myocardial infarction. Early detection of diagnostic features underlying electrocardiography signals is crucial for the identification and treatment of CAD. In the present work, we proposed novel entropy called Renyi Distribution Entropy (RdisEn) for the analysis of short-term heart rate variability (HRV) signals and the detection of CAD. Our simulation experiment with synthetic, physiological, and pathological signals demonstrated that RdisEn could distinguish effectively among different subject groups. Compared to the values of sample entropy or approximation entropy, the RdisEn value was less affected by the parameter choice, and it remained stable even in short-term HRV. We have developed a combined CAD detection scheme with RdisEn and wavelet packet decomposition (WPD): (1) Normal and CAD HRV beats obtained were divided into two equal parts. (2) Feature acquisition: RdisEn and WPD-based statistical features were calculated from one part of HRV beats, and student's t-test was performed to select clinically significant features. (3) Classification: selected features were computed from the remaining part of HRV beats and fed into K-nearest neighbor and support vector machine, to separate CAD from normal subjects. The proposed scheme automatically detected CAD with 97.5% accuracy, 100% sensitivity and 95% specificity and performed better than most of the existing schemes.
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