IEEE Trans Biomed Eng. 2021 May;68(5):1496-1506. doi: 10.1109/TBME.2020.3028041. Epub 2021 Apr 21.
In this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (SpO ) signals, is proposed. The detector consists of two parts: one that detects reductions in amplitude fluctuation of PPG (DAP)and one that detects oxygen desaturations. To further differentiate among sleep disordered breathing events (SDBE), the pulse rate variability (PRV) was extracted from the PPG signal, and then used to extract features that enhance the sympatho-vagal arousals during apneas and hypopneas. A classification was performed to discriminate between central and obstructive events, apneas and hypopneas. The algorithms were tested on 96 overnight signals recorded at the UZ Leuven hospital, annotated by clinical experts, and from patients without any kind of co-morbidity. An accuracy of 75.1% for the detection of apneas and hypopneas, in one-minute segments,was reached. The classification of the detected events showed 92.6% accuracy in separating central from obstructive apnea, 83.7% for central apnea and central hypopnea and 82.7% for obstructive apnea and obstructive hypopnea. The low implementation cost showed a potential for the proposed method of being used as screening device, in ambulatory scenarios.
本工作提出了一种利用光电容积脉搏波（PPG）和外周血氧饱和度（SpO ）信号检测和分类睡眠呼吸暂停和低通气的方法。该探测器由两部分组成：一部分用于检测 PPG 幅度波动的降低（DAP），另一部分用于检测氧饱和度降低。为了进一步区分睡眠呼吸障碍事件（SDBE），从 PPG 信号中提取脉搏率变异性（PRV），然后用于提取特征，以增强呼吸暂停和低通气期间的交感神经-迷走神经兴奋。对来自无合并症患者的 96 个在 UZ 鲁汶医院记录的、由临床专家标注的过夜信号进行了分类，以区分中枢性和阻塞性事件、呼吸暂停和低通气。在一分钟的片段中，检测呼吸暂停和低通气的准确率达到了 75.1%。检测到的事件的分类在区分中枢性和阻塞性呼吸暂停方面的准确率为 92.6%，在区分中枢性呼吸暂停和中枢性低通气以及阻塞性呼吸暂停和阻塞性低通气方面的准确率为 82.7%。该方法的低实施成本表明，它有可能在非卧床场景中用作筛查设备。
