Markov models and heart rate variability hidden dynamic

The hidden dynamic of the 24-hour HRV time series extracted from the ECG records of 7 patients with different cardiac pathologies is investigated. The underlying structure of each 1-hour HRV subsequence is approximated by using Markov models with minimum order n. Such minimum order supplies a measure of the HRV's nonlinearity degree and of the underlying nervous system during each examined hour. The minimum Markov order's evolution is then investigated over the 24 hours. During the night a relatively stable minimum Markov order can be observed. The different pathologies seem to exhibit different minimum Markov order time evolutions. Finally VT episodes can be located inside periods of low nonlinear activity of the autonomic nervous system. The minimum Markov order shows to be a reliable index for quantifying the risk factor associated with the HRV parameter.