A new approach to detect congestive heart failure using Teager energy nonlinear scatter plot of R-R interval series

Area of Science:

• Cardiology
• Biomedical Engineering
• Nonlinear Dynamics

Background:

• Heart rate variability (HRV) is significantly impaired in patients with congestive heart failure (CHF).
• Understanding the nonlinear dynamics of HRV is crucial for identifying cardiac dysfunction.
• Existing methods may not fully capture the complex regulatory mechanisms affecting HRV in CHF.

Purpose of the Study:

• To develop a novel approach for distinguishing CHF subjects from healthy individuals.
• To investigate the utility of Teager energy of R-R interval series in revealing cardiac patterning.
• To quantify the effectiveness of this new method compared to traditional approaches.

Main Methods:

• Construction of a nonlinear scatter plot using the Teager energy of R-R interval series.
• Analysis of signal energy at the source of generation, rather than signal energy itself.
• Introduction of curvilinearity measures and a radial distance index (RDI) for quantitative comparison.
• Utilizing a k-nearest neighbor classifier with RDI as the primary feature.

Main Results:

• The Teager energy scatter plot demonstrates significant qualitative and quantitative differences between normal and CHF subjects.
• The radial distance index effectively highlights the superiority of the Teager energy scatter plot over the second-order difference plot (SODP).
• The k-nearest neighbor classifier achieved an almost 100% classification rate when using RDI as a feature.

Conclusions:

• The Teager energy scatter plot of R-R intervals offers a powerful and accurate method for CHF detection.
• This nonlinear approach effectively captures deviations in HRV dynamics caused by complex physiological regulations.
• The proposed method shows high potential for clinical application in diagnosing congestive heart failure.