Sleep staging from Heart Rate Variability: time-varying spectral features and Hidden Markov Models
by Martin Oswaldo Mendez, Matteo Matteucci, Vincenza Castronovo, Luigi Ferini-Strambi, Sergio Cerutti, Anna Maria Bianchi
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 3, No. 3/4, 2010

Abstract: 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.

Online publication date:: Tue, 13-Apr-2010

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com