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Related Experiment Video

Updated: May 14, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

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Multiscale sample entropy based on discrete wavelet transform for clinical heart rate variability recognition.

Ming-Yuan Lee¹, Sung-Nien Yu

¹Department of Electrical Engineering, National Chung Cheng University, Chia-Yi County, Taiwan. a39025309@hotmail.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

This study introduces discrete wavelet transform (DWT) for analyzing heart rate variability signals to detect congestive heart failure (CHF) and atrial fibrillation (AF), improving accuracy over traditional methods.

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Area of Science:

Biomedical Engineering
Signal Processing
Cardiology

Background:

Traditional multiscale methods use coarse-grained averaging (CGA) for sample entropy (SE) analysis.
Accurate characterization of heart rate variability (HRV) is crucial for diagnosing cardiac conditions.

Purpose of the Study:

To propose discrete wavelet transform (DWT) for multiscale SE feature extraction from HRV signals.
To enhance the recognition of congestive heart failure (CHF) and atrial fibrillation (AF) from normal sinus rhythm (NSR).

Main Methods:

Decomposition of HRV signals into multiscale sequences using DWT.
Calculation of SE features from DWT-decomposed sequences.
Classification using Support Vector Machine (SVM) with leave-one-out cross-validation.

Main Results:

The DWT-based multiscale SE method with five dyadic scales outperformed traditional CGA methods.
The proposed 5-scale DWT method improved CHF recognition by 6.7% and AF recognition by 0.77% compared to 5-scale CGA.
Overall recognition accuracy increased by 8.35% using the 5-scale DWT method.

Conclusions:

DWT offers a more effective approach for multiscale SE feature extraction in HRV analysis.
The proposed DWT-based method significantly enhances the diagnostic accuracy for CHF and AF.