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Dynamical systems and fractal geometry applied to cardiac dynamics analysis in the Peruvian population
Sandra C Correa1, Laura D Ardila1, Signed Esperanza Prieto2
1Grupo Armonía, Centro de Investigación y Atención Psicosocial Hanami, Bogotá, Colombia.
Insights
This study adapted dynamical systems and fractal geometry for cardiovascular risk stratification in Peru. Adjusting diagnostic limits improved accuracy and specificity for the Peruvian population, though sensitivity decreased.
Area of Science:
- Cardiovascular health
- Dynamical systems theory
- Fractal geometry
Background:
- Cardiovascular diseases pose a significant public health challenge in Peru.
- Effective risk stratification methods tailored to the Peruvian population are needed.
- Dynamical systems and fractal geometry show potential for risk stratification in other populations.
Purpose of the Study:
- To adjust diagnostic limits for cardiovascular normality and disease in Peru.
- To apply a methodology based on dynamical systems and fractal geometry.
- To improve risk stratification accuracy for the Peruvian population.
Main Methods:
- Recorded 24-hour heart rate and beats per hour in 272 individuals (193 normal, 79 with arrhythmias) via Holter studies.
- Measured spatial occupation of attractors and fractal dimensions to determine mathematical states.
- Compared results using Cohen's Kappa coefficient and adjusted normality/disease limits.
Main Results:
- Original limits: sensitivity 0.595, specificity 0.653, accuracy 0.636. Kappa (2x2): 0.219.
- Adjusted limits: sensitivity 0.430, specificity 0.839, accuracy 0.721. Kappa (2x2): 0.285.
- Adjusting limits enhanced specificity but reduced sensitivity, improving overall agreement.
Conclusions:
- The adapted method shows improved agreement with conventional diagnosis for the Peruvian population.
- The adjusted limits demonstrate a fair level of alignment, with increased specificity.
- Further research is necessary to validate clinical applicability.
Introduction:
Cardiovascular diseases in Peru are a significant public health problem, and effective methods for risk stratification are needed. Using dynamical systems and fractal geometry shows promising results in other populations.
Objective:
To adjust the limits between normality and disease in diagnoses according to the characteristics of the Peruvian population, using a methodology based on dynamical systems and fractal geometry.
Methodology:
Heart rate and beats per hour were recorded over 24 h in 272 cases, 193 normal and 79 with arrhythmias, from Holter studies. Spatial occupation of attractors and their fractal dimensions were measured to determine its mathematical state. Results were compared using Cohen's Kappa coefficient with respect to conventional diagnosis. The limits of normality and disease were adjusted to improve concordance with the standard for the Peruvian population.
Results:
With the original limits, sensitivity was 0.595, specificity was 0.653, positive predictive value was 0.412, negative predictive value was 0.797, and accuracy was 0.636. The Kappa for the 2 × 2 table was 0.219 (95% CI, 0.104-0.334) and for the 3 × 3 table, it was 0.141 (95% CI, 0.050-0.221). By adjusting the limits, sensibility was 0.430, specificity was 0.839, positive predictive value was 0.523, negative predictive value was 0.783, and accuracy was 0.721. The Kappa for the 2 × 2 table was 0.285 (95% CI, 0.164-0.409) and for the 3 × 3, 0.123 (95% CI, 0.036-0.209).
Conclusion:
The agreement of the method improved with the new limits, demonstrating a fair level of alignment, characterized by enhanced specificity but reduced sensitivity. More studies are needed for clinical application.
