Adaptive correlation dimension method for analysing heart rate variability during the menstrual cycle.

Area of Science:

• Physiology
• Nonlinear dynamics
• Biomedical signal processing

Background:

• Heart rate variability (HRV) analysis is crucial for understanding autonomic nervous system function.
• Conventional correlation dimension (CD) methods for nonlinear HRV analysis suffer from inaccurate time delay estimations.
• These inaccuracies limit the precise assessment of HRV variations, especially across physiological cycles like the menstrual cycle.

Purpose of the Study:

• To introduce an adaptive correlation dimension (CD) method for enhanced nonlinear heart rate variability (HRV) analysis.
• To optimize the calculation of time delays within HRV signals for improved accuracy.
• To validate the proposed method's efficacy in detecting subtle HRV changes and differentiating physiological states.

Main Methods:

• The proposed adaptive CD method segments HRV time series into overlapping windows.
• Optimal time delays are calculated for each window using an adaptive autocorrelation technique based on signal information content.
• CD is computed for each window with optimal delays, and the final adaptive CD is the average of these values.

Main Results:

• The adaptive CD method accurately differentiates between normal and diseased subjects.
• The method demonstrates superior accuracy in detecting HRV variations during the menstrual cycle in both lying and standing postures.
• Comparative analysis confirms the superiority of adaptive CD over conventional CD and detrended fluctuation analyses.

Conclusions:

• The adaptive correlation dimension method provides a more accurate estimation of time delays in HRV analysis.
• This enhanced accuracy leads to improved detection of physiological changes and disease states.
• The proposed method represents a significant advancement in nonlinear HRV analysis, particularly for dynamic physiological processes.