Uddin Md B, Moi Chow Chin, Ling Sai H, Su Steven W
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5339-5342. doi: 10.1109/EMBC44109.2020.9176212.
Sleep apnea is a common sleep disorder that can significantly decrease the quality of life. An accurate and early diagnosis of sleep apnea is required before getting proper treatment. A reliable automated detection of sleep apnea can overcome the problems of manual diagnosis (scoring) due to variability in recording and scoring criteria (for example across Europe) and to inter-scorer variability. This study explored a novel automated algorithm to detect apnea and hypopnea events from airflow and pulse oximetry signals, extracted from 30 polysomnography records of the Sleep Heart Health Study. Apneas and hypopneas were manually scored by a trained sleep physiologist according to the updated 2017 American Academy of Sleep Medicine respiratory scoring rules. From pre-processed airflow, the peak signal excursion was precisely determined from the peak-to-trough amplitude using a sliding window, with a per-sample digitized algorithm for detecting apnea and hypopnea. For apnea, the peak signal excursion drop was operationalized at ≥85% and for hypopnea at ≥35% of its pre-event baseline. Using backward shifting of oximetry, hypopneas were filtered with ≥3% oxygen desaturation from its baseline. The performance of the automated algorithm was evaluated by comparing the detection with manual scoring (a standard practice). The sensitivity and positive predictive value of detecting apneas and hypopneas were respectively 98.1% and 95.3%. This automated algorithm is applicable to any portable sleep monitoring device for the accurate detection of sleep apnea.
睡眠呼吸暂停是一种常见的睡眠障碍，会显著降低生活质量。在进行适当治疗之前，需要对睡眠呼吸暂停进行准确的早期诊断。可靠的睡眠呼吸暂停自动检测可以克服手动诊断（评分）中由于记录和评分标准的差异（例如在欧洲各地）以及评分者之间的差异而产生的问题。本研究探索了一种新颖的自动算法，用于从睡眠心脏健康研究的30份多导睡眠图记录中提取的气流和脉搏血氧饱和度信号中检测呼吸暂停和呼吸不足事件。呼吸暂停和呼吸不足由一名训练有素的睡眠生理学家根据2017年更新的美国睡眠医学学会呼吸评分规则进行手动评分。从预处理后的气流中，使用滑动窗口从峰谷振幅精确确定峰值信号偏移，并采用逐样本数字化算法检测呼吸暂停和呼吸不足。对于呼吸暂停，峰值信号偏移下降在其事件前基线的≥85%时判定，对于呼吸不足，在≥35%时判定。通过使用血氧饱和度的后移，将基线氧饱和度下降≥3%的呼吸不足进行过滤。通过将检测结果与手动评分（一种标准做法）进行比较，评估了自动算法的性能。检测呼吸暂停和呼吸不足的灵敏度和阳性预测值分别为98.1%和95.3%。这种自动算法适用于任何便携式睡眠监测设备，以准确检测睡眠呼吸暂停。
