't Wallant Dorothée Coppieters, Muto Vincenzo, Gaggioni Giulia, Jaspar Mathieu, Chellappa Sarah L, Meyer Christelle, Vandewalle Gilles, Maquet Pierre, Phillips Christophe
Cyclotron Research Centre, University of Liège, Allée du 6 Août 8 B30, B-4000 Sart-Tilman, Belgium; Department of Electrical Engineering and Computer Science, University of Liège, Allée de la découverte 10 B28, B-4000 Liège, Belgium.
Cyclotron Research Centre, University of Liège, Allée du 6 Août 8 B30, B-4000 Sart-Tilman, Belgium; Department of Psychology: Cognition and Behaviour, University of Liège, Place des Orateurs 2, B32B-4000 Liège, Belgium; Walloon Excellence in Lifesciences and Biotechnology (WELBIO), Avenue Pasteur 6, B-1300 Wavre, Belgium.
J Neurosci Methods. 2016 Jan 30;258:124-33. doi: 10.1016/j.jneumeth.2015.11.005. Epub 2015 Nov 14.
In sleep electroencephalographic (EEG) signals, artifacts and arousals marking are usually part of the processing. This visual inspection by a human expert has two main drawbacks: it is very time consuming and subjective.
To detect artifacts and arousals in a reliable, systematic and reproducible automatic way, we developed an automatic detection based on time and frequency analysis with adapted thresholds derived from data themselves.
The automatic detection performance is assessed using 5 statistic parameters, on 60 whole night sleep recordings coming from 35 healthy volunteers (male and female) aged between 19 and 26. The proposed approach proves its robustness against inter- and intra-, subjects and raters' scorings, variability. The agreement with human raters is rated overall from substantial to excellent and provides a significantly more reliable method than between human raters.
Existing methods detect only specific artifacts or only arousals, and/or these methods are validated on short episodes of sleep recordings, making it difficult to compare with our whole night results.
The method works on a whole night recording and is fully automatic, reproducible, and reliable. Furthermore the implementation of the method will be made available online as open source code.
在睡眠脑电图（EEG）信号中，伪迹和觉醒标记通常是处理过程的一部分。由专家进行的这种目视检查有两个主要缺点：非常耗时且主观。
为了以可靠、系统且可重复的自动方式检测伪迹和觉醒，我们开发了一种基于时间和频率分析的自动检测方法，并从数据本身得出了适配的阈值。
使用5个统计参数，对来自年龄在19至26岁之间的35名健康志愿者（男性和女性）的60份整夜睡眠记录评估了自动检测性能。所提出的方法证明了其在受试者之间和受试者内部、评分者评分以及变异性方面的稳健性。与人工评分者的一致性总体上被评为从显著到优秀，并且提供了一种比人工评分者之间显著更可靠的方法。
现有方法仅检测特定伪迹或仅检测觉醒，和/或这些方法在短睡眠记录片段上得到验证，这使得难以与我们的整夜结果进行比较。
该方法适用于整夜记录，并且是完全自动、可重复且可靠的。此外，该方法的实现将作为开源代码在线提供。
