Hidden Markov Models and Mutual Information Analysis to Characterize Nonlinear Dynamics in Heart Rate Variability

A study of nonlinear dynamics of the heart rate variability (HRV) was performed using hidden Markov models (HMM) and mutual information (Ml). A methodology based on HMM has been developed in the present work. Cardiac RR series were analyzed in the three frequency bands: HF (0.15-0.45 Hz), high frequency band; LF (0.04-0.15 Hz), low frequency band; VLF (0.003-0.04 Hz), very low frequency band. These series (O, observations) were modeled using HMM. The model lambda=(A,B,pi) was selected so that P(O/lambda) was locally maximized. Ergodic topology and N=10 states were also considered for this analysis. Different measures based on HMM were defined and obtained from RR time series of 37 idiopathic dilated cardiomyopathy (IDC) patients and 46 healthy subjects (NRM), during awake and sleep stages. Two groups of IDC patients were considered: 11 high risk (HR) patients, after aborted sudden cardiac death (SCD) or who died during the follow up; 26 low risk (LR) patients, without SCD. Some HMM measures showed high percentages (up to 100%) of well classified subjects in all groups