Raiesdana Somayeh
Faculty of Electrical, Biomedical and Mechatronic Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Australas Phys Eng Sci Med. 2018 Mar;41(1):161-176. doi: 10.1007/s13246-018-0624-0. Epub 2018 Feb 8.
An automated sleep staging based on analyzing long-range time correlations in EEG is proposed. These correlations, indicating time-scale invariant property or self-similarity at different time scales, are known to be salient dynamical characteristics of stage succession for a sleeping brain even when the subject suffers a destructive disorder such as Obstructive Sleep Apnea (OSA). The goal is to extract a set of complementary features from cerebral sources mapped onto the scalp electrodes or from a number of denoised EEG channels. For this purpose, source localization/extraction and noise reduction approaches based on Independent Component Analysis were used prior to correlation analysis. Feature extracted segments were then classified in one of the five classes including WAKE, STAGE1, STAGE2, SWS and REM via an ensemble neuro-fuzzy classifier. Some techniques were employed to improve the classifier's performance including Scaled Conjugate Gradient Method to speed up learning the ANFIS classifiers, a pruning algorithm to eliminate irrelevant fuzzy rules and the 10-fold cross-validation technique to train and test the system more efficiently. The performance of classification for two strategies including (1) feature extraction from effective cerebral sources and (2) feature extraction from selected channels of denoised EEG signals was compared and contrasted in terms of training errors and test accuracies. The first and second strategies achieved 92.23 and 88.74% agreement with human expert respectively which indicates the effectiveness of the staging system based on cerebral sources of activity. Our results further indicate that the misclassification rates were almost below 10%. The proposed automated sleep staging system is reliable due to the fact that it is based on the underlying dynamics of sleep staging which is less likely to be affected by sleep fragmentations occurred repeatedly in OSA.
提出了一种基于分析脑电图（EEG）中长程时间相关性的自动睡眠分期方法。这些相关性表明了不同时间尺度上的时间尺度不变性或自相似性，即使受试者患有诸如阻塞性睡眠呼吸暂停（OSA）等破坏性疾病，它们也是睡眠大脑阶段更替的显著动态特征。目标是从映射到头皮电极的脑源或多个去噪后的EEG通道中提取一组互补特征。为此，在相关性分析之前，使用了基于独立成分分析的源定位/提取和降噪方法。然后，通过集成神经模糊分类器将提取的特征段分类为包括清醒、1期、2期、慢波睡眠和快速眼动睡眠在内的五个类别之一。采用了一些技术来提高分类器的性能，包括使用缩放共轭梯度法来加速自适应神经模糊推理系统（ANFIS）分类器的学习、使用剪枝算法消除无关的模糊规则以及使用10折交叉验证技术更有效地训练和测试系统。从训练误差和测试准确率方面对两种策略的分类性能进行了比较和对比，这两种策略分别是：（1）从有效的脑源中提取特征；（2）从去噪后的EEG信号的选定通道中提取特征。第一种和第二种策略分别与人类专家的判断达成了92.23%和88.74%的一致性，这表明基于脑活动源的分期系统是有效的。我们的结果进一步表明，错误分类率几乎低于10%。所提出的自动睡眠分期系统是可靠的，因为它基于睡眠分期的潜在动力学，不太可能受到OSA中反复出现的睡眠片段化的影响。
