A Universal Scaling Relation for Defining Power Spectral Bands in Mammalian Heart Rate Variability Analysis.

Area of Science:

• Cardiovascular Physiology
• Autonomic Nervous System Research
• Comparative Mammalian Physiology

Background:

• Power spectral density (PSD) analysis of heart rate intervals quantifies cardiovascular control via frequency bands (VLF, LF, HF).
• Human PSD bands are empirically standardized, but mammal-specific cutoffs lack quantitative justification for animal model research.
• Accurate PSD band definition is crucial for using heart rate (HR) analysis as a proxy for autonomic nervous system (ANS) activity in diverse species.

Purpose of the Study:

• To develop a quantitative, data-driven method for defining mammal-specific frequency bands (VLF, LF, HF) in heart rate variability (HRV) analysis.
• To establish scaling laws for PSD band cutoffs across mammals based on heart rate and body mass.
• To enable reliable non-invasive assessment of ANS function in animal models using HRV PSD analysis.

Main Methods:

• Utilized a Gaussian mixture model (GMM) to identify prominent frequency peaks in normalized PSD from human, dog, and mouse ECG recordings.
• Trained and validated the GMM algorithm on existing datasets, then applied it to predict PSD bands for rabbits.
• Analyzed scaling relationships between GMM-identified cutoff frequencies, typical heart rate (HRm), and body mass (BMm) using double-logarithmic plots.

Main Results:

• Identified scaling laws: fVLF-LF = 0.0037⋅HRm^(−1/4), fLF-HF = 0.0017⋅HRm^(−1/8), and fHFup = 0.0128⋅HRm^(−1/4).
• Demonstrated that PSD band cutoff frequencies and Gaussian means exhibit allometric scaling with body mass.
• Successfully predicted novel PSD frequency bands for rabbits, validating the model's predictive power.

Conclusions:

• An automated, data-driven approach successfully defined mammal-specific PSD frequency bands for HRV analysis.
• The derived scaling laws between band cutoffs and HRm provide a method to approximate PSD bands in various mammalian species.
• This methodology advances the use of HRV PSD analysis for non-invasive ANS assessment in preclinical research and comparative physiology.