Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging

Authors:
S. Charbonnier;L. Zoubek;S. Lesecq;F. Chapotot
Affiliations:
Gipsa-lab, Control System Department, BP 46, F-38 402 Saint Martin d'Hères Cedex, France;Department of Information and Communication Technologies, Pedagogical Faculty, University of Ostrava, eskobratrská 16, 701 03 Ostrava, Czech Republic;CEA-LETI MINATEC, 17 rue des Martyrs, F-38054 Grenoble Cedex 9, France;Department of Medicine, University of Chicago, Chicago, IL, USA
Venue:
Computers in Biology and Medicine
Year:
2011

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Abstract

An automatic sleep/wake stages classifier that deals with the presence of artifacts and that provides a confidence index with each decision is proposed. The decision system is composed of two stages: the first stage checks the 20s epoch of polysomnographic signals (EEG, EOG and EMG) for the presence of artifacts and selects the artifact-free signals. The second stage classifies the epoch using one classifier selected out of four, using feature inputs extracted from the artifact-free signals only. A confidence index is associated with each decision made, depending on the classifier used and on the class assigned, so that the user's confidence in the automatic decision is increased. The two-stage system was tested on a large database of 46 night recordings. It reached 85.5% of overall accuracy with improved ability to discern NREM I stage from REM sleep. It was shown that only 7% of the database was classified with a low confidence index, and thus should be re-evaluated by a physiologist expert, which makes the system an efficient decision-support tool.