A methodological comparison of the Porges algorithm, fast Fourier transform, and autoregressive spectral analysis for the estimation of heart rate variability in 5-month-old infants


  • We would like to acknowledge the use of the Porges machine from Professor Blain Ditto's laboratory. We thank Jocelyn Malo for coordinating the data collection and Hélène Paradis, Bernadette Simoneau, and Jacqueline Langlois for their assistance in data management. We are grateful to the participating families. This research was supported by grants from the National Health Research Development Program, the Social Sciences and Humanities Research Council of Canada, the Canadian Institutes of Health Research, the Canada Research Chair program, the Fonds Québécois de la Recherche sur la Société et la Culture, and the Fonds de la Recherche en Santé du Québec.


Little empirical evidence exists on the comparability of heart rate variability (HRV) quantification methods commonly used in infants. The aim was to compare three methods of HRV estimation: (1) fast Fourier transform (FFT), (2) autoregressive (AR), and (3) the Porges methods. HRV was estimated in 63 healthy 5-month-old infants. HRV parameters were strongly correlated across methods (.92–.99) but yielded significantly different mean HRV estimates (Porges method > FFT > AR). There was no systematic bias over the whole range of values between the two spectral approaches, while differences between the Porges method and the spectral estimates were systematically greater for larger values. Additional comparative studies are needed to explore the between-method agreement across a range of physiological conditions.