PDF(Pubmed)
Abstract：
Transient increases in ventilation induced by arousal from sleep during Cheyne-Stokes respiration in heart failure patients are thought to contribute to sustaining and exacerbating the ventilatory oscillation. The only possibility to investigate the validity of this notion is to use observational data. This entails some significant challenges: (i) accurate identification of both arousal onset and offset; (ii) detection of short arousals (<3 s); (iii) breath-by-breath analysis of the interaction between arousals and ventilation; (iv) careful control for important confounding factors. In this paper we report how we have tackled these challenges by developing innovative computer-assisted methodologies. The identification of arousal onset and offset is performed by a hybrid approach that integrates visual scoring with computer-based automated analysis. We use a statistical detector to automatically discriminate between dominant theta-delta and dominant alpha activity at each instant of time. Moreover, a statistical detector is used to validate visual scoring of K complexes, delta waves or artifacts associated with an EEG frequency shift, as well as frequency shifts to beta activity. A high-resolution (250 ms) state-transition diagram providing continuous information on the sleep-wake state of the subject is finally obtained. Based on this information, arousals are automatically identified as any state change from sleep to wakefulness lasting ≥2 s. The assessment of the interaction between arousals and ventilation is performed using a breath-by-breath, case-control approach. The arousal-associated change in ventilation is measured as the normalized difference between minute ventilation in the case breath (i.e., with arousal) and that in the control breath (i.e., without arousal), controlling for sleep stage and chemical drive. The latter is estimated by using information from pulse oximetry at the finger. In the last part of the paper, we discuss main potential sources of error inherent in the described methodologies.
摘要：
心力衰竭患者在Cheyne-Stokes呼吸期间由睡眠唤醒引起的通气短暂增加被认为有助于维持和加剧通气振荡。调查这一概念有效性的唯一可能性是使用观测数据。这带来了一些重大挑战：（i）准确识别唤醒发作和抵消；（ii）检测短暂的唤醒（<3s）；（iii）对唤醒和通气之间的相互作用进行逐呼吸分析；（iv）仔细控制重要的混杂因素。在本文中，我们报告了如何通过开发创新的计算机辅助方法来应对这些挑战。通过将视觉评分与基于计算机的自动分析相结合的混合方法来执行唤醒开始和偏移的识别。我们使用统计检测器在每个时刻自动区分主要的theta-delta和主要的alpha活性。此外，统计检测器用于验证K复合物的视觉评分，与EEG频移相关的delta波或伪影，以及β活性的频率偏移。最终获得提供关于对象的睡眠-觉醒状态的连续信息的高分辨率(250ms)状态转变图。根据这些信息,唤醒被自动识别为从睡眠到觉醒持续≥2s的任何状态变化。唤醒和通气之间的相互作用的评估是使用逐次呼吸进行的，病例控制方法。通气的唤醒相关变化被测量为在呼吸情况下的分钟通气之间的归一化差异（即，唤醒)和控制呼吸(即，没有唤醒)，控制睡眠阶段和化学驱动。后者是通过使用来自手指处的脉搏血氧饱和度的信息来估计的。在论文的最后一部分,我们讨论了所描述的方法中固有的主要潜在误差源。
