Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability

Area of Science:

• Biomedical Engineering
• Physiological Signal Processing
• Complexity Science

Background:

• Electrocardiogram (ECG) signals are crucial for analyzing heart rate variability (HRV) complexity.
• Traditional entropy methods like multiscale entropy (MSE) using sample entropy (SampEn) face limitations with short time series.
• Distribution entropy (DistEn) has emerged as a more stable alternative for short time series analysis.

Purpose of the Study:

• To introduce a novel multiscale distribution entropy (MDE) method for analyzing short-term HRV complexity.
• To enhance the stability and reliability of entropy evaluation for HRV derived from ECG.
• To assess the performance of MDE compared to existing methods like MSE.

Main Methods:

• Developed a novel multiscale DistEn (MDE) approach.
• Utilized a moving-averaging multiscale process combined with DistEn computation.
• Applied MDE to synthetic signals for performance verification and compared it with MSE.
• Evaluated MDE on short-term HRV data from ECG signals of congestive heart failure (CHF) patients and healthy subjects.

Main Results:

• MDE demonstrated superior performance compared to MSE in analyzing synthetic signals.
• MDE effectively quantified the reduced complexity of HRV in aging individuals.
• MDE successfully identified decreased HRV complexity associated with congestive heart failure (CHF).

Conclusions:

• MDE provides a stable and reliable method for assessing short-term HRV complexity from ECG.
• The proposed MDE method is capable of detecting changes in HRV complexity related to aging and CHF.
• MDE offers a promising tool for clinical applications requiring analysis of short-duration physiological signals.