Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method.
Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method.
复制标题
DOI：
10.1093/sleep/30.12.1756
复制
发表时间：
2007-12
期刊：
影响因子：
5.6
通讯作者：
R. Thomas;J. Mietus;Chung-Kang Peng;G. Gilmartin;Robert W Daly;A. Goldberger;D. Gottlieb
中科院分区：
文献类型：
--
作者：
R. Thomas;J. Mietus;Chung-Kang Peng;G. Gilmartin;Robert W Daly;A. Goldberger;D. Gottlieb
复杂睡眠呼吸暂停被定义为继发于上呼吸道阻塞和呼吸控制功能障碍的睡眠呼吸障碍。本研究的目的是评估基于心电图（ECG）的心肺耦合技术在区分阻塞性、中枢性或复合性睡眠呼吸暂停中的应用价值。对存档的多导睡眠图数据集进行分析。计算机信号分析实验室。干预措施。测量和结果：PhysioNet睡眠呼吸暂停数据库由70张多导联睡眠图（包括持续约8小时的单导联心电图信号）组成，用于训练基于心电图的自主神经和呼吸相互作用（心肺耦合）测量，以检测低频耦合（e-LFC）升高的呼吸暂停和低呼吸期。在PhysioNet BIDMC充血性心力衰竭数据库（15例受试者的心电图）中，“窄谱带”e-LFC模式尤为常见。然后将该算法应用于睡眠心脏健康研究- i数据集，以选择具有最高数量的宽窄谱带e-LFC的15条记录。后者的频谱特征似乎不仅可以检测中枢性呼吸暂停，还可以检测具有周期性呼吸模式的阻塞性低通气。将该算法应用于77个睡眠实验室分夜研究表明，窄带e-LFC的存在预示着正压通气诱导中枢性呼吸暂停的敏感性增加。结论：基于脑电图的频谱分析可以自动、独立于操作者的表征呼吸控制障碍和上气道解剖性梗阻之间可能的相互作用。光谱表型的临床应用，特别是在预测气道正压治疗失败方面，仍有待更彻底的测试。
STUDY OBJECTIVES Complex sleep apnea is defined as sleep disordered breathing secondary to simultaneous upper airway obstruction and respiratory control dysfunction. The objective of this study was to assess the utility of an electrocardiogram (ECG)-based cardiopulmonary coupling technique to distinguish obstructive from central or complex sleep apnea. DESIGN Analysis of archived polysomnographic datasets. SETTING A laboratory for computational signal analysis. INTERVENTIONS None. MEASUREMENTS AND RESULTS The PhysioNet Sleep Apnea Database, consisting of 70 polysomnograms including single-lead ECG signals of approximately 8 hours duration, was used to train an ECG-based measure of autonomic and respiratory interactions (cardiopulmonary coupling) to detect periods of apnea and hypopnea, based on the presence of elevated low-frequency coupling (e-LFC). In the PhysioNet BIDMC Congestive Heart Failure Database (ECGs of 15 subjects), a pattern of "narrow spectral band" e-LFC was especially common. The algorithm was then applied to the Sleep Heart Health Study-I dataset, to select the 15 records with the highest amounts of broad and narrow spectral band e-LFC. The latter spectral characteristic seemed to detect not only periods of central apnea, but also obstructive hypopneas with a periodic breathing pattern. Applying the algorithm to 77 sleep laboratory split-night studies showed that the presence of narrow band e-LFC predicted an increased sensitivity to induction of central apneas by positive airway pressure. CONCLUSIONS ECG-based spectral analysis allows automated, operator-independent characterization of probable interactions between respiratory dyscontrol and upper airway anatomical obstruction. The clinical utility of spectrographic phenotyping, especially in predicting failure of positive airway pressure therapy, remains to be more thoroughly tested.