Recurrence quantification analysis on pulse morphological changes in patients with coronary heart disease

Area of Science:

• Cardiovascular Medicine
• Traditional Chinese Medicine
• Biomedical Engineering
• Nonlinear Dynamics

Background:

• Pulse diagnosis is a cornerstone of Traditional Chinese Medicine (TCM).
• Nonlinear dynamic analysis offers advanced methods for signal processing.
• Integrating TCM pulse diagnosis with modern analytical techniques can reveal novel insights into cardiovascular health.

Purpose of the Study:

• To investigate the efficacy of TCM pulse diagnosis combined with nonlinear dynamic analysis for identifying cardiovascular diseases.
• To compare the diagnostic accuracy of nonlinear dynamic analysis of pulse signals with traditional methods.

Main Methods:

• Recurrence quantification analysis (RQA) was applied to pulse morphological data from 37 coronary heart disease (CHD) patients and 37 healthy controls.
• Independent sample t-tests were used to compare RQA measures between groups.
• Support vector machine (SVM) models were trained using RQA measures for classification.
• Classic time-domain parameters were used as a comparative analysis.

Main Results:

• RQA measures revealed significant differences in pulse morphology between CHD patients and controls (P < 0.05).
• Specific RQA parameters showed increased regularity, determinism, and stability in CHD patients.
• SVM classification achieved 88.21% accuracy using RQA measures, outperforming traditional time-domain parameters (75.73%).

Conclusions:

• Nonlinear dynamic analysis, specifically RQA, provides a noninvasive method to assess pulse signal changes.
• Pulse morphology in individuals with CHD exhibits greater regularity and less variability compared to healthy individuals.
• RQA-based pulse analysis demonstrates high accuracy for early diagnosis of cardiovascular diseases like CHD.