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通过心率变异性分析鉴别儿童中与睡眠呼吸暂停相关的光电容积脉搏波信号幅度波动降低情况。

Discrimination of sleep-apnea-related decreases in the amplitude fluctuations of PPG signal in children by HRV analysis.

作者信息

Gil Eduardo, Mendez Martín, Vergara José María, Cerutti Sergio, Bianchi Anna Maria, Laguna Pablo

机构信息

Communications Technology Group, Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza 50009, Spain.

出版信息

IEEE Trans Biomed Eng. 2009 Apr;56(4):1005-14. doi: 10.1109/TBME.2008.2009340. Epub 2008 Nov 11.

Abstract

In this paper, an analysis of heart rate variability (HRV) during decreases in the amplitude fluctuations of photopletysmography (PPG) [decreases in the amplitude fluctuations of photopletysmography (DAP)] events for obstructive sleep apnea syndrome (OSAS) screening is presented. Two hundred and sixty-eight selected signal segments around the DAP event were extracted and classified in five groups depending on SaO (2) and respiratory behavior. Four windows around each DAP are defined and temporal evolution of time-frequency HRV parameters was analyzed for OSAS screening. Results show a significant increase in sympathetic activity during DAP events, which is higher in cases associated with apnea. DAP events were classified as apneic or nonapneic using a linear discriminant analysis from the HRV indexes. The ratio of DAP events per hour r(DAP) and the ratio of apneic DAP events per hour r(DAP)(a) were computed. Results show an accuracy of 79% for r(DAP)(a) (12% increase with respect to r(DAP)), a sensitivity of 87.5%, and a specificity of 71.4% when classifying 1-h polysomnographic excerpts. As for clinical subject classification, an accuracy of 80% (improvement of 6.7% ), a sensitivity of 87.5%, and a specificity of 71.4% are reached. These results suggest that the combination of DAP and HRV could be an improved alternative for sleep apnea screening from PPG with the added benefit of its low cost and simplicity.

摘要

本文提出了一种通过分析光电容积脉搏波描记法(PPG)幅度波动下降期间的心率变异性(HRV)[光电容积脉搏波描记法幅度波动下降(DAP)]事件来筛查阻塞性睡眠呼吸暂停综合征(OSAS)的方法。提取了DAP事件周围268个选定的信号段,并根据血氧饱和度(SaO₂)和呼吸行为分为五组。在每个DAP周围定义了四个窗口,并分析了时频HRV参数的时间演变以进行OSAS筛查。结果显示,DAP事件期间交感神经活动显著增加,在与呼吸暂停相关的病例中更高。利用HRV指标通过线性判别分析将DAP事件分类为呼吸暂停或非呼吸暂停。计算了每小时DAP事件的比率r(DAP)和每小时呼吸暂停DAP事件的比率r(DAP)(a)。结果显示,在对1小时多导睡眠图摘录进行分类时,r(DAP)(a)的准确率为79%(相对于r(DAP)提高了12%),灵敏度为87.5%,特异性为71.4%。对于临床受试者分类,准确率达到80%(提高了6.7%),灵敏度为87.5%,特异性为71.4%。这些结果表明,DAP和HRV的结合可能是一种改进的PPG睡眠呼吸暂停筛查方法,具有低成本和简单的额外优势。

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