Comput. Biol. Med. - Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging.

Tópicos

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Resumo

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.

Resumo Limpo

automat sleepwak stage classifi deal presenc artifact provid confid index decis propos decis system compos two stage first stage check s epoch polysomnograph signal eeg eog emg presenc artifact select artifactfre signal second stage classifi epoch use one classifi select four use featur input extract artifactfre signal confid index associ decis made depend classifi use class assign user confid automat decis increas twostag system test larg databas night record reach overal accuraci improv abil discern nrem stage rem sleep shown databas classifi low confid index thus reevalu physiologist expert make system effici decisionsupport tool

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