School of Biomedical Engineering, Faculty of Engineering & Information Technology, University of Technology Sydney, Sydney, Australia.
Centre for Health Technologies, University of Technology Sydney, Sydney, Australia.
Physiol Meas. 2022 Jun 30;43(6). doi: 10.1088/1361-6579/ac6b11.
. Sleep apnea is a common sleep breathing disorder that can significantly decrease sleep quality and have major health consequences. It is diagnosed based on the apnea hypopnea index (AHI). This study explored a novel, generalized algorithm for the automatic diagnosis of sleep apnea employing airflow (AF) and oximetry (SpO) signals.. Of the 988 polysomnography records, 45 were randomly selected for developing the automatic algorithm and the remainder 943 for validating purposes. The algorithm detects apnea events by a per-sample encoding process applied to the peak excursion of AF signal. Hypopnea events were detected from the per-sample encoding of AF and SpOwith an adjustment to time lag in SpO. Total recording time was automatically processed and optimized for computation of total sleep time (TST). Total number of detected events and computed TST were used to estimate AHI. The estimated AHI was validated against the scored data from the Sleep Heart Health Study.. Intraclass correlation coefficient of 0.94 was obtained between estimated and scored AHIs. The diagnostic accuracies were 93.5%, 92.4%, and 96.6% for AHI cut-off values of ≥5, ≥15, and ≥30 respectively. The overall accuracy for the combined severity categories (normal, mild, moderate, and severe) and kappa were 83.4% and 0.77 respectively.. This new automatic technique was found to be superior to the other existing methods and can be applied to any portable sleep devices especially for home sleep apnea tests.
睡眠呼吸暂停是一种常见的睡眠呼吸障碍，会显著降低睡眠质量，并对健康产生重大影响。它是基于呼吸暂停低通气指数（AHI）来诊断的。本研究探索了一种新的、通用的算法，该算法利用气流（AF）和血氧饱和度（SpO）信号自动诊断睡眠呼吸暂停。在 988 份多导睡眠图记录中，随机选择 45 份用于开发自动算法，其余 943 份用于验证目的。该算法通过对 AF 信号峰值偏移的逐样本编码过程来检测呼吸暂停事件。呼吸暂停事件是通过对 AF 和 SpO 的逐样本编码来检测的，并对 SpO 的时间滞后进行调整。总记录时间自动处理，并优化用于计算总睡眠时间（TST）。检测到的事件总数和计算出的 TST 用于估计 AHI。估计的 AHI 与睡眠心脏健康研究的评分数据进行了验证。估计的 AHI 和评分的 AHI 之间的组内相关系数为 0.94。对于 AHI 截断值分别为≥5、≥15 和≥30，诊断准确率分别为 93.5%、92.4%和 96.6%。对于综合严重程度类别（正常、轻度、中度和重度）和kappa 的总准确率分别为 83.4%和 0.77。发现这种新的自动技术优于其他现有的方法，可应用于任何便携式睡眠设备，特别是用于家庭睡眠呼吸暂停测试。
