A study on the nonlinear dynamics of PR interval variability using surrogate data

PR interval variability (PRV) has been proposed as a noninvasive tool for investigating the autonomic nervous system (ANS) as well as heart rate variability (HRV). The goal of this study is to determine whether PRV has nonlinear dynamics or not. The authors developed an automatic PR interval measurement algorithm, and tested it using MIT ECG databases. 24-hour Holter ECGs were acquired from 30 healthy adults. Surrogate data was generated from a simpler linear model to have similar statistical characteristics with the original data. To detect nonlinear dynamics of the time series, correlation dimension, Lyapunov exponent, and phase space reconstruction were evaluated in both data. The results showed that PRV was significantly different from the surrogate data in the tests. It was concluded that PR interval variability shows nonlinear behavior