Sleep staging from heart rate variability: Time-varying spectral features and hidden markov models

An alternative DSS which models the behaviour of the Heart Rate Variability (HRV) signal linked to stable (NREM) and instable (REM) cerebral waves during sleep and a probabilistic model of the sleep stages transitions for decision was developed. Time-Varying Autoregressive Models (TVAMs) were used as feature extractor while Hidden Markov Models (HMM) was used as time series classifier. 24 full polysomnography recordings from healthy sleepers were used for the analysis and those were separated in two sets of 12 each: training and test set. The classification performance for the test set was specificity = 0.851, accuracy = 0.793 and sensitivity = 0.702. Copyright © 2010 Inderscience Enterprises Ltd.

Decision-support systems; DSS; Heart rate variability; Hidden Markov model; HMM; HRV; Sleep; Sleep staging; Time-varying analysis Artificial intelligence; Classification (of information); Decision support systems; Heart; Sleep research; Trellis codes; Classification performance; Feature extractor; Heart rate variability; Probabilistic modeling; Sleep; Sleep staging; Time varying analysis; Time varying autoregressive model; Hidden Markov models

VIVO