Continuous Unsupervised Sleep Staging Based on a Single EEG Signal

Authors:
Arthur Flexer;Georg Gruber;Georg Dorffner
Affiliations:
-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 2
Cited 1

System identification (2nd ed.): theory for the user

System identification (2nd ed.): theory for the user
A Probabilistic Approach to High-Resolution Sleep Analysis

ICANN '01 Proceedings of the International Conference on Artificial Neural Networks

A reliable probabilistic sleep stager based on a single EEG signal

Artificial Intelligence in Medicine

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Abstract

We report improvements on automatic continuous sleep staging using Hidden Markov Models (HMM). Our totally unsupervised approach detects the cornerstones of human sleep (wakefulness, deep and rem sleep) with around80% accuracy basedon data from a single EEG channel. Contrary to our previous efforts we trainedthe HMM on data from a single sleep lab instead of generalizing to data from diverse sleep labs. This solvedour previous problem of detecting rem sleep.