Faculty of Sleep Medicine and Telemedicine, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.
Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.
Sleep Breath. 2021 Dec;25(4):1945-1952. doi: 10.1007/s11325-021-02316-0. Epub 2021 Feb 16.
In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB).
Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0).
We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen's kappa of 0.62 was found for this 3-class classification problem.
The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.
在这项原理验证研究中,我们评估了基于动作和呼吸感应体积描记(RIP)带中提取的特征,使用新型 Nox BodySleep 1.0 算法(Nox Medical,冰岛)估计疾病严重程度和睡眠阶段的诊断准确性。在睡眠呼吸障碍(SDB)患者中,通过实验室多导睡眠图(PSG)进行验证。
患者根据 AASM 接受 PSG。使用 AASM 标准手动对睡眠阶段进行评分,并使用新型算法对记录进行评估。使用 IBM SPSS Statistics 25.0 对结果进行描述性统计分析(IBM SPSS Statistics 25.0)。
我们发现 AHI 估计的 Pearson 相关系数(r=0.91)有 0.2/h 的偏差,总睡眠时间(TST)的相关性较好(r=0.81)且高估 14 分钟。睡眠效率(SE)的 Pearson 相关性也较好(r=0.73),高估 2.1%。觉醒期的估计敏感性为 0.65,特异性为 0.59,而 REM 和非 REM(NREM)期的评估敏感性分别为 0.72 和 0.74,特异性分别为 0.74 和 0.68。此外,对于这个 3 类分类问题,还发现 Cohen's kappa 值为 0.62。
该算法在估计睡眠方面具有中等诊断准确性。此外,该算法与手动评分具有良好的一致性,在估计觉醒-睡眠转换方面具有良好的诊断准确性。该算法似乎是提高便携式监测诊断准确性的合适工具。该验证诊断算法有望成为一种更合适、更具成本效益的方法,如果集成在疑似 SDB 患者的中心外(OOC)测试中。