[Time-frequency Research of Pulse Rate Variability Based on the EEMD]

Area of Science:

• Biomedical Engineering
• Signal Processing
• Physiology

Background:

• Non-stationary and nonlinear signals, such as pulse rate variability (PRV), pose challenges for traditional analysis methods.
• Understanding autonomic nervous system activity (sympathetic and vagus nerves) is crucial for diagnosing various health conditions.

Purpose of the Study:

• To develop and validate a novel method for analyzing PRV signals using Ensemble Empirical Mode Decomposition (EEMD).
• To accurately quantify high-frequency energy (EHF), low-frequency energy (ELF), and very low-frequency energy (EVLF) of PRV signals.
• To assess the correlation between EEMD-based PRV analysis and traditional methods for reflecting autonomic nerve activity.

Main Methods:

• Decomposition of PRV signals into intrinsic mode function (IMF) components using EEMD.
• Calculation of energy for each IMF component.
• Reconstruction of EHF, ELF, and EVLF from IMF component energies based on spectral characteristics.
• Comparison of the proposed EEMD method with the traditional AR power spectrum estimation method.

Main Results:

• The EEMD-based method successfully decomposed PRV signals into relevant IMF components.
• Reconstructed EHF, ELF, and EVLF parameters showed high correlation ( > 0.96) with those obtained by the traditional AR power spectrum estimation method.
• The high correlation indicates the proposed method's ability to accurately represent autonomic nerve activity.

Conclusions:

• EEMD is a suitable technique for analyzing complex, non-stationary PRV signals.
• The novel EEMD-based approach provides a reliable method for quantifying PRV frequency domain energy.
• This method offers a valid way to assess sympathetic and vagus nerve activity through PRV analysis.