Universidade da Coruña, Departamento de Computación, Facultade de Informática, Campus de Elviña s/n, 15071, A Coruña, Spain.
Sleep Center and Clinical Neurophysiology, Haaglanden Medisch Centrum, Lijnbaan 32, 2512 VA, The Hague, The Netherlands.
Comput Biol Med. 2017 Aug 1;87:77-86. doi: 10.1016/j.compbiomed.2017.05.011. Epub 2017 May 13.
Clinical diagnosis of sleep disorders relies on the polysomnographic test to examine the neurophysiological markers of the sleep process. In this test, the recording of the electroencephalographic activity and the submental electromyogram is the source of the analysis for the detection of electroencephalographic arousals. The identification of these events is important for the evaluation of the sleep continuity because they cause the fragmentation of the normal sleep process. This work proposes a new technique for the automatic detection of arousals in polysomnographic recordings, presenting a non-computationally complex method with the idea of providing an easy integration with other algorithms.
The proposed algorithm combines different well-known signal analysis solutions to identify relevant arousal patterns with special emphasis on robustness and artifacts tolerance. It is a multistage method that after obtaining an initial set of events, improves the detection finding common EEG arousal patterns. Finally, false positives are discarded after examining each candidate within the context of clinical definitions.
22 polysomnographic recordings from real patients were used to validate the method. The results obtained were encouraging, achieving a precision value of 0.86 and a F score value of 0.79. When compared with the gold standard, the method achieves a substantial agreement (Kappa coefficient of 0.78), with an almost perfect agreement with ten recordings.
The algorithm designed achieved encouraging results and shows robust behavior in presence of signal artifacts. Its low-coupled design allows its implementation on different development platforms, and an easy combination with other methods.
睡眠障碍的临床诊断依赖于多导睡眠图测试来检查睡眠过程的神经生理标志物。在该测试中，脑电图活动和颏下肌电图的记录是检测脑电图唤醒的分析来源。这些事件的识别对于评估睡眠连续性非常重要，因为它们会导致正常睡眠过程的碎片化。这项工作提出了一种新的多导睡眠图记录中自动检测唤醒的技术，提出了一种非计算复杂的方法，旨在与其他算法易于集成。
所提出的算法结合了不同的著名信号分析解决方案，以识别具有特殊强调稳健性和抗干扰能力的相关唤醒模式。它是一种多阶段的方法，在获得初始事件集后，通过检查每个候选者在临床定义的上下文中来改进检测结果，找到常见的脑电图唤醒模式。最后，在丢弃假阳性后。
使用 22 名来自真实患者的多导睡眠图记录来验证该方法。所获得的结果令人鼓舞，达到了 0.86 的精度值和 0.79 的 F 分数值。与金标准相比，该方法具有显著的一致性（Kappa 系数为 0.78），其中有十份记录几乎完全一致。
设计的算法取得了令人鼓舞的结果，并在存在信号干扰的情况下表现出稳健的行为。其低耦合设计允许在不同的开发平台上实现，并与其他方法易于结合。
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