Comparison of R-R interval prediction models.

Area of Science:

• Cardiology
• Physiology
• Data Science

Background:

• R-R interval patterns are crucial for electrocardiogram (ECG) arrhythmia detection.
• Understanding these patterns is vital for basic physiological research.
• Automated monitoring uses R-R interval prediction and pattern deviation detection.

Purpose of the Study:

• To clarify the utility of Markov processes in predicting R-R interval changes.
• To compare the accuracy of linear and nonlinear regression models for R-R interval analysis.
• To identify advantages of different modeling approaches in ECG interpretation.

Main Methods:

• Application of Markov processes for R-R interval sequence analysis.
• Implementation and comparison of linear regression models.
• Implementation and comparison of nonlinear regression models.

Main Results:

• Markov processes demonstrate significant usefulness in predicting R-R interval dynamics.
• Both linear and nonlinear regression models offer valuable insights into R-R interval patterns.
• Specific advantages were identified for both linear and nonlinear regression approaches.

Conclusions:

• Markov processes are effective tools for analyzing and predicting R-R interval variations in ECG.
• A comparative analysis of regression models provides guidance for selecting appropriate analytical methods.
• The findings enhance automated arrhythmia detection strategies through improved pattern recognition.