An energy screening and morphology characterization-based hybrid expert scheme for automatic identification of micro-sleep event K-complex
An energy screening and morphology characterization-based hybrid expert scheme for automatic identification of micro-sleep event K-complex
复制标题
DOI：
10.1016/j.cmpb.2021.105955
复制
发表时间：
2021-01
影响因子：
6.1
通讯作者：
Xian Zhao;Chen Chen-Chen;Wei Zhou;Yalin Wang;Jiahao Fan;Zeyu Wang;Saeed Akbarzadeh;W. Chen
中科院分区：
文献类型：
--
作者：
Xian Zhao;Chen Chen-Chen;Wei Zhou;Yalin Wang;Jiahao Fan;Zeyu Wang;Saeed Akbarzadeh;W. Chen
背景与目的：K复合物作为睡眠分期和睡眠保护的重要指标，是睡眠分析中的重要微事件。临床上，K 复合体是通过专家在睡眠期间对脑电图 (EEG) 进行目视检查来识别的。由于这个过程很费力并且观察者之间的变异性很高，因此开发自动化 K 复合物检测方法可以减轻临床医生的负担，同时提供可靠的识别结果。然而，现有方法面临以下问题。首先，大多数工作仅识别第二阶段的 K 复合体，这需要区分睡眠阶段作为进一步识别事件的先决条件。其次，大多数方法只能检测事件的发生，而无法预测事件的位置和持续时间，而这对于睡眠分析也是至关重要的。 方法：在这项工作中，通过将信号形态与专家知识集成到决策过程中，提出了一种用于 K 复合体检测的新型混合专家方案。为了消除伪影，并最大限度地减少原始睡眠脑电图信号的个体差异，首先通过结合 Teager 能量算子 (TEO) 和个性化阈值来筛选潜在的 K 复合体候选者。然后，为了区分信号形状和背景活动，设计了一种基于形态滤波（MF）的新型滤波框架，以区分 EEG 系列中 K 复合波形的形态成分。最后，受微睡眠事件专家知识的启发，通过判断规则从提取的形态信息中识别出 K 复合波形。结果：通过在公共数据库 MASS-C1（蒙特利尔睡眠研究档案第一队列）上的应用来评估检测性能，该数据库包括 19 名健康成年人的记录。检测性能显示平均 F 值为 0.63，召回率为 0.81，精度为 0.53。事件和检测之间的持续时间误差为0.10 s。结论：所提出的方案已经检测到事件的发生。同时，它已经识别了它们的位置和持续时间。有利的结果表明，所提出的方案优于最先进的研究，并且具有帮助减轻专家在睡眠脑电图分析方面的负担的巨大潜力。
Background and Objective:K-complexes, as a significant indicator in sleep staging and sleep protection, are an important micro-event in sleep analysis. Clinically, K-complexes are recognized through the expert visual inspection of electroencephalogram (EEG) during sleep. Since this process is laborious and has high inter-observer variability, developing automated K-complex detection methods can alleviate the burden on clinicians while providing reliable recognition results. However, existing methods face the following issues. First, most work only identifies the K-complexes in stage 2, which requires distinguishing the sleep stages as the prerequisite for further events’ identification. Second, most approaches can only detect the occurrence of events without the ability to predict their location and duration, which are also essential to sleep analysis.Methods:In this work, a novel hybrid expert scheme for K-complex detection is proposed by integrating signal morphology with expert knowledge into the decision-making process. To eliminate artifacts, and to minimize the individual variability in raw sleep EEG signals, the potential K-complex candidates are first screened by combining Teager energy operator (TEO) and personalized thresholds. Then, to distinguish signal shapes from background activity, a novel frame of filtering based on morphological filtering (MF) is devised to differentiate morphological components of K-complex waveforms from EEG series. Finally, K-complex waveforms are identified from the extracted morphological information by judgment rules, which are inspired by expert knowledge of micro-sleep events.Results:Detection performance is evaluated by its application on the public database MASS-C1 (Montreal archives of sleep studies cohort one) which includes the recordings of 19 healthy adults. The detection performance demonstrates an F-measure of 0.63 with a recall of 0.81 and a precision of 0.53 on average. The duration error between events and detections is 0.10 s.Conclusions:The presented scheme has detected the occurrence of events. Meanwhile, it has recognized their locations and durations. The favorable results exhibit that the proposed scheme outperforms the state-of-the-art studies and has great potential to help release the burden of experts in sleep EEG analysis.