Karmakar Chandan, Khandoker Ahsan, Penzel Thomas, Schöbel Christoph, Palaniswami Marimuthu
IEEE J Biomed Health Inform. 2014 May;18(3):1065-73. doi: 10.1109/JBHI.2013.2282338. Epub 2013 Sep 23.
Respiratory events during sleep induce cortical arousals and manifest changes in autonomic markers in sleep disorder breathing (SDB). Finger photoplethysmography (PPG) has been shown to be a reliable method of determining sympathetic activation. We hypothesize that changes in PPG signals are sufficient to predict the occurrence of respiratory-event-related cortical arousal. In this study, we develop a respiratory arousal detection model in SDB subjects by using PPG features. PPG signals from 10 SDB subjects (9 male, 1 female) with age range 43-75 years were used in this study. Time domain features of PPG signals, such as 1) PWA--pulse wave amplitude, 2) PPI--peak-to-peak interval, and 3) Area--area under peak, were used to detect arousal events. In this study, PWA and Area have shown better performance (higher accuracy and lower false rate) compared to PPI features. After investigating possible groupings of these features, combination of PWA and Area (PWA + Area) was shown to provide better accuracy with a lower false detection rate in arousal detection. PPG-based arousal indexes agreed well across a wide range of decision thresholds, resulting in a receiver operating characteristic with an area under the curve of 0.91. For the decision threshold (PC(thresh) = 25%) chosen for the final analyses, a sensitivity of 68.1% and a specificity of 95.2% were obtained. The results showed an accuracy of 84.68%, 85.15%, 86.93%, and 50.79% with a false rate of 21.80%, 55.41%, 64.78%, and 50.79% at PC(thresh) = 25% or PPI, PWA, Area , and PWA + Area features, respectively. This indicates that combining PWA and Area features reduced the false positive rate without much affecting the sensitivity of the arousal detection system. In conclusion, the PPG-based respiratory arousal detection model is a simple and promising alternative to the conventional electroencephalogram (EEG)-based respiratory arousal detection system.
睡眠期间的呼吸事件会引发皮层觉醒，并在睡眠呼吸障碍（SDB）中表现为自主神经标志物的变化。手指光电容积脉搏波描记法（PPG）已被证明是确定交感神经激活的可靠方法。我们假设PPG信号的变化足以预测与呼吸事件相关的皮层觉醒的发生。在本研究中，我们通过使用PPG特征为SDB受试者开发了一种呼吸觉醒检测模型。本研究使用了10名年龄在43 - 75岁之间的SDB受试者（9名男性，1名女性）的PPG信号。PPG信号的时域特征，如1）PWA——脉搏波振幅、2）PPI——峰峰值间隔和3）Area——峰值下面积，被用于检测觉醒事件。在本研究中，与PPI特征相比，PWA和Area表现出更好的性能（更高的准确率和更低的错误率）。在研究了这些特征可能的分组后，PWA和Area的组合（PWA + Area）在觉醒检测中显示出更高的准确率和更低的错误检测率。基于PPG的觉醒指数在广泛的决策阈值范围内具有良好的一致性，从而得到了曲线下面积为0.91的受试者工作特征曲线。对于最终分析选择的决策阈值（PC(thresh) = 25%），获得了68.1%的灵敏度和95.2%的特异性。结果显示，在PC(thresh) = 25%时，使用PPI、PWA、Area和PWA + Area特征的准确率分别为84.68%、85.15%、86.93%和50.79%，错误率分别为21.80%、55.41%、64.78%和50.79%。这表明结合PWA和Area特征降低了假阳性率，而对觉醒检测系统的灵敏度影响不大。总之，基于PPG的呼吸觉醒检测模型是传统基于脑电图（EEG）的呼吸觉醒检测系统的一种简单且有前景的替代方法。
