A reliable probabilistic sleep stager based on a single EEG signal

Authors:
Arthur Flexer;Georg Gruber;Georg Dorffner
Affiliations:
The Austrian Research Institute for Artificial Intelligence, Freyung 6/6, A-1010 Vienna, Austria and Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of C ...;The Austrian Research Institute for Artificial Intelligence, Freyung 6/6, A-1010 Vienna, Austria;The Austrian Research Institute for Artificial Intelligence, Freyung 6/6, A-1010 Vienna, Austria and Department of Medical Cybernetics and Artificial Intelligence, Medical University of Vienna, Fr ...
Venue:
Artificial Intelligence in Medicine
Year:
2005

Citing 4
Cited 8

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

System identification (2nd ed.): theory for the user
Continuous Unsupervised Sleep Staging Based on a Single EEG Signal

ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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

Processing of signals recorded through smart devices: sleep-quality assessment

IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Automatic sleep stage identification: difficulties and possible solutions

HIKM '10 Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management - Volume 108
A simple feature reduction method for the detection of long biological signals

CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A reliable probabilistic sleep stager based on a single EEG signal

Artificial Intelligence in Medicine
Sleep stage classification using unsupervised feature learning

Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Extracting more information from EEG recordings for a better description of sleep

Computer Methods and Programs in Biomedicine
An ensemble system for automatic sleep stage classification using single channel EEG signal

Computers in Biology and Medicine
A method for the automatic analysis of the sleep macrostructure in continuum

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Objective: We developed a probabilistic continuous sleep stager based on Hidden Markov models using only a single EEG signal. It offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1s instead of 30s), and being based on solid probabilistic principles rather than a predefined set of rules (Rechtschaffen & Kales) Methods and material: Sixty-eight whole night sleep recordings from two different sleep labs are analysed using Gaussian observation Hidden Markov models. Results: Our unsupervised approach detects the cornerstones of human sleep (wakefulness, deep and rem sleep) with around 80% accuracy based on data from a single EEG channel. There are some difficulties in generalizing results across sleep labs. Conclusion: Using data from a single electrode is sufficient for reliable continuous sleep staging. Sleep recordings from different sleep labs are not directly comparable. Training of separate models for the sleep labs is necessary.