title: Sleep staging classification based on HRV: time-variant analysis.
abstract: An algorithm to evaluate the sleep macrostructure based on heart rate fluctuations from ECG signal is presented. This algorithm is an attempt to evaluate the sleep quality out of sleep centers. The algorithm is made up by a) a time-variant autoregressive model used as feature extractor and b) a hidden Markov model used as classifier. Characteristics coming from the joint probability of HRV features were used to fed the HMM. 17 full polysomnography recordings from healthy subjects were used in the current analysis. When compared to Wake-NREM-REM given by experts, the automatic classifier achieved a total accuracy of 78.21+/-6.44% and a kappa index of 0.41+/-.1085 using two features and a total accuracy of 79.43+/-8.83% and kappa index of 0.42+/-.1493 using three features.
authors: Politecnico di Milano, Milano, IT 20133 Italia. martin.mendez@biomed.polimi.it
authors: M O
authors: MO
authors: Mendez
authors: M
authors: M
authors: Matteucci
authors: S
authors: S
authors: Cerutti
authors: F
authors: F
authors: Aletti
authors: A M
authors: AM
authors: Bianchi
mesh_terms: D000328:Adult; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012894:Sleep Stages / Q000502:physiology*