Sudden drops in pulse wave amplitude (PWA) measured by finger photoplethysmography (PPG) are known to reflect peripheral vasoconstriction resulting from sympathetic activation. Previous work demonstrated that sympathetic activations during sleep typically accompany the occurrence of pathological respiratory and motor events, and their alteration may be associated with the arising of metabolic and cardiovascular diseases. Importantly, PWA-drops often occur in the absence of visually identifiable cortical micro-arousals and may thus represent a more accurate marker of sleep disruption/fragmentation. In this light, an objective and reproducible quantification and characterization of sleep-related PWA-drops may offer a valuable, non-invasive approach for the diagnostic and prognostic evaluation of patients with sleep disorders. However, the manual identification of PWA-drops represents a time-consuming practice potentially associated with high intra/inter-scorer variability. Since validated algorithms are not readily available for research and clinical purposes, here we present a novel automated approach to detect and characterize significant drops in the PWA-signal. The algorithm was tested against expert human scorers who visually inspected corresponding PPG-recordings. Results demonstrated that the algorithm reliably detects PWA-drops and is able to characterize them in terms of parameters with a potential physiological and clinical relevance, including timing, amplitude, duration and slopes. The method is completely user-independent, processes all-night PSG-data, automatically dealing with potential artefacts, sensor loss/displacements, and stage-dependent variability in PWA-time-series. Such characteristics make this method a valuable candidate for the comparative investigation of large clinical datasets, to gain a better insight into the reciprocal links between sympathetic activity, sleep-related alterations, and metabolic and cardiovascular diseases.

中文翻译：

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通过用于脉搏波幅度下降检测的自动方法来量化睡眠期间的外周交感神经激活。

已知通过手指光电容积描记法（PPG）测量的脉搏波幅度（PWA）突然下降反映了由交感神经激活引起的外周血管收缩。先前的研究表明，睡眠过程中的交感神经激活通常伴随病理性呼吸和运动事件的发生，并且它们的改变可能与代谢性疾病和心血管疾病的发生有关。重要的是，PWA下降通常在没有视觉上可识别的皮质微耳的情况下发生，因此可能代表了更准确的睡眠破坏/碎片化标志。有鉴于此，对与睡眠有关的PWA-drops进行客观，可重复的定量和表征可为睡眠障碍患者的诊断和预后评估提供有价值的非侵入性方法。然而，手动识别PWA-drops代表一项耗时的实践，可能与高的内部/得分间差异相关。由于经过验证的算法尚无法用于研究和临床目的，因此在此我们提出了一种新颖的自动化方法来检测和表征PWA信号中的显着下降。该算法针对专业的人类评分员进行了测试，这些评分员目视检查了相应的PPG记录。结果表明，该算法能够可靠地检测PWA滴，并能够根据具有潜在生理和临床相关性的参数（包括时间，幅度，持续时间和斜率）对它们进行表征。该方法完全独立于用户，可整夜处理PSG数据，自动处理潜在的伪像，传感器丢失/位移，和PWA时间序列中与阶段有关的可变性。这种特性使该方法成为大型临床数据集比较研究的有价值的候选者，从而可以更好地了解交感神经活动，睡眠相关改变以及代谢性和心血管疾病之间的相互关系。