Pinna Gian Domenico, Maestri Roberto
Laboratory for the Study of Ventilatory Instability, Department of Biomedical Engineering, Montescano Institute - IRCCS, Istituti Clinici Scientifici Maugeri, Montescano, Italy.
Front Physiol. 2022 Feb 10;13:815352. doi: 10.3389/fphys.2022.815352. eCollection 2022.
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.
心力衰竭患者在潮式呼吸期间因睡眠觉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。研究这一观点有效性的唯一可能性是使用观察数据。这带来了一些重大挑战：（i）准确识别觉醒的开始和结束；（ii）检测短觉醒（<3秒）；（iii）逐次呼吸分析觉醒与通气之间的相互作用；（iv）仔细控制重要的混杂因素。在本文中，我们报告了我们如何通过开发创新的计算机辅助方法来应对这些挑战。觉醒开始和结束的识别通过一种混合方法进行，该方法将视觉评分与基于计算机的自动分析相结合。我们使用统计检测器在每个时刻自动区分主要的θ-δ和主要的α活动。此外，还使用统计检测器来验证K复合波、δ波或与脑电图频率变化相关的伪迹以及向β活动的频率变化的视觉评分。最终获得了一个高分辨率（250毫秒）的状态转换图，提供了关于受试者睡眠-觉醒状态的连续信息。基于此信息，觉醒被自动识别为从睡眠到觉醒持续≥2秒的任何状态变化。使用逐次呼吸的病例对照方法评估觉醒与通气之间的相互作用。将与觉醒相关的通气变化测量为病例呼吸（即有觉醒时）的分钟通气量与对照呼吸（即无觉醒时）之间的标准化差异，同时控制睡眠阶段和化学驱动。后者通过使用手指脉搏血氧饱和度的信息来估计。在本文的最后一部分，我们讨论了所描述方法中固有的主要潜在误差来源。
