Title :
Using hidden Markov models to build an automatic, continuous and probabilistic sleep stager
Author :
Flexer, A. ; Sykacek, P. ; Rezek, I. ; Dorffner, G.
Author_Institution :
Austrian Res. Inst. for Artificial Intelligence, Vienna, Austria
Abstract :
We report about an automatic continuous sleep stager which is based on probabilistic principles employing hidden Markov models (HMMs). Our sleep stager offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1 second instead of 30 second): and being based on solid probabilistic principles rather than a predefined set of rules. Results obtained for nine whole night sleep recordings are reported
Keywords :
covariance matrices; electroencephalography; electromyography; hidden Markov models; neural nets; probability; sleep; time series; automatic continuous probabilistic sleep stager; probabilistic principles; temporal resolution; whole night sleep recordings; Artificial intelligence; Electroencephalography; Electromyography; Electrooculography; Hidden Markov models; Humans; Intelligent robots; Probability distribution; Robotics and automation; Solids;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861392
