Practical problems of determining the dimensions of heart rate data

Area of Science:

• Physiology
• Nonlinear dynamics
• Data analysis

Background:

• Phase space reconstruction is crucial for analyzing complex physiological time series.
• Understanding heart rate variability (HRV) dynamics can reveal health status.
• Previous studies often face challenges with data limitations and non-stationarity.

Purpose of the Study:

• To investigate the practical challenges in estimating phase space dimensions from rabbit heartbeat data.
• To evaluate the influence of data construction methods and data properties on dimension measures.
• To identify optimal conditions for obtaining reproducible dimension estimates.

Main Methods:

• Analysis of real, experimental, and simulated heartbeat time series data.
• Application of various phase space reconstruction techniques.
• Assessment of different dimension estimation measures.
• Evaluation of data properties: amount, noise, trends, and stationarity.

Main Results:

• Dimension estimation is sensitive to phase space construction methods.
• Data properties like noise and non-stationarity significantly impact dimension measures.
• Reproducible dimension estimates require large datasets and stationary conditions.
• Differences in dimension were observed between normal and diseased rabbits.

Conclusions:

• Accurate phase space dimension estimation in physiological systems is data-dependent.
• Stationary, extensive datasets are essential for reliable analysis of heart rate dynamics.
• Methodological choices in phase space reconstruction critically affect results.
• This work provides insights for analyzing complex biological data.