Gipsa-lab, Control System Department, BP 46, F-38 402 Saint Martin d'Hères Cedex, France.
Comput Biol Med. 2011 Jun;41(6):380-9. doi: 10.1016/j.compbiomed.2011.04.001. Epub 2011 Apr 16.
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.
提出了一种自动睡眠/觉醒阶段分类器，该分类器可以处理伪影的存在，并为每个决策提供置信指数。决策系统由两个阶段组成：第一阶段检查多导睡眠图信号（EEG、EOG 和 EMG）的 20s 时程，以检测伪影的存在，并选择无伪影的信号。第二阶段仅使用从无伪影信号中提取的特征输入，从四个分类器中选择一个分类器对时程进行分类。根据使用的分类器和分配的类别，为每个决策关联一个置信指数，从而提高用户对自动决策的信心。该两阶段系统在一个包含 46 个夜间记录的大型数据库上进行了测试。它的总体准确率达到了 85.5%，并且能够更好地区分 NREM I 期和 REM 睡眠。结果表明，数据库中只有 7%的记录被分类为低置信指数，因此应由生理学家专家重新评估，这使得该系统成为一个有效的决策支持工具。
