The gold standard for assessing sleep apnea, polysomnography, is resource intensive and inconvenient. Thus, several simpler alternatives have been proposed. However, validations of these alternatives have focused primarily on estimating the apnea-hypopnea index (apnea events per hour of sleep), which means information, clearly important from a physiological point of view such as apnea type, apnea duration, and temporal distribution of events, is lost. The purpose of the present study was to investigate if this information could also be provided with the combination of radar technology and pulse oximetry by classifying sleep apnea events on a second-by-second basis. Fourteen patients referred to home sleep apnea testing by their medical doctor were enrolled in the study (6 controls and 8 patients with sleep apnea; 4 mild, 2 moderate, and 2 severe) and monitored by Somnofy (radar-based sleep monitor) in parallel with respiratory polygraphy. A neural network was trained on data from Somnofy and pulse oximetry against the polygraphy scorings using leave-one-subject-out cross-validation. Cohen’s kappa for second-by-second classifications of no event/event was 0.81, or almost perfect agreement. For classifying no event/hypopnea/apnea and no event/hypopnea/obstructive apnea/central apnea/mixed apnea, Cohen’s kappa was 0.43 (moderate agreement) and 0.36 (fair agreement), respectively. The Bland-Altman 95% limits of agreement for the respiratory event index (apnea events per hour of recording) were -8.25 and 7.47, and all participants were correctly classified in terms of sleep apnea severity. Furthermore, the results showed that the combination of radar and pulse oximetry could be more accurate than the two technologies separately. Overall, the results indicate that radar technology and pulse oximetry could reliably provide information on a second-by-second basis for no event/event which could be valuable for management of sleep apnea. To be clinically useful, a larger study is necessary to validate the algorithm on a general population.

中文翻译：

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使用非接触式雷达技术、脉搏血氧仪和机器学习检测个体睡眠呼吸暂停事件的试点研究

评估睡眠呼吸暂停的黄金标准多导睡眠图是资源密集型且不方便的。因此，已经提出了几种更简单的替代方案。然而，这些替代方案的验证主要集中在估计呼吸暂停低通气指数（每小时睡眠呼吸暂停事件），这意味着信息，从生理学的角度来看显然很重要，例如呼吸暂停类型、呼吸暂停持续时间和事件的时间分布，丢失了。本研究的目的是通过逐秒对睡眠呼吸暂停事件进行分类，研究是否也可以通过雷达技术和脉搏血氧饱和度的组合来提供此信息。该研究招募了 14 名由医生转介到家庭睡眠呼吸暂停测试的患者（6 名对照组和 8 名患有睡眠呼吸暂停的患者；4 名轻度、2 名中度、和 2 个严重）并由 Somnofy（基于雷达的睡眠监测器）与呼吸测谎仪并行监测。神经网络使用来自 Somnofy 的数据和脉搏血氧饱和度测量仪的数据与使用留一主题交叉验证的测谎评分进行训练。Cohen 对无事件/事件的逐秒分类的 kappa 为 0.81，或几乎完全一致。对于无事件/低通气/呼吸暂停和无事件/低通气/阻塞性呼吸暂停/中枢性呼吸暂停/混合性呼吸暂停的分类，Cohen's kappa 分别为 0.43（中等一致性）和 0.36（一般一致性）。呼吸事件指数（每小时记录的呼吸暂停事件）的 Bland-Altman 95% 一致性限制为 -8.25 和 7.47，并且所有参与者都根据睡眠呼吸暂停严重程度进行了正确分类。此外，结果表明，雷达和脉搏血氧饱和度的结合比这两种技术单独使用更准确。总体而言，结果表明雷达技术和脉搏血氧饱和度可以可靠地为无事件/事件提供逐秒的信息，这对于睡眠呼吸暂停的管理可能很有价值。为了在临床上有用，需要进行更大规模的研究以在一般人群中验证该算法。