Applicability of autoregressive spectral analysis for heart rate variability in dogs: validation of model order and comparison with fast Fourier transform

Abstract

Heart rate variability (HRV) analysis is widely used to evaluate autonomic nervous system activity. Although a fixed 16th-order autoregressive (AR) model is commonly used for human short-term frequency-domain HRV analysis, its applicability to dogs has not been validated. This study evaluated the suitability of the fixed 16th-order AR model for canine HRV analysis by comparing HRV indices obtained using AR(16) with those derived from Akaike’s Information Criterion (AIC)- and Bayesian Information Criterion (BIC)-selected AR models and the conventional fast Fourier transform (FFT) method. Twenty-three clinically healthy dogs underwent continuous heart rate monitoring for approximately 3 days using a wearable device. One stationary 5-min R-R interval segment was selected from each recording, and frequency-domain HRV indices were calculated using AR(16), AIC-selected AR models, BIC-selected AR models, and FFT. Agreement among methods was evaluated using intraclass correlation coefficients and Bland–Altman analysis. HRV indices obtained using AR(16) showed excellent agreement with AIC-selected model orders and good agreement with FFT, whereas agreement with BIC-selected model orders was lower, particularly for low-frequency-related indices. These findings support the applicability of the conventional fixed 16th-order AR model for canine frequency-domain HRV analysis and provide methodological validation for its use in future veterinary HRV research.