Thomas Robert Joseph, Mietus Joseph E, Peng Chung-Kang, Gilmartin Geoffrey, Daly Robert W, Goldberger Ary L, Gottlieb Daniel J
Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center Boston, MA 02215, USA.
Sleep. 2007 Dec;30(12):1756-69. doi: 10.1093/sleep/30.12.1756.
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.
Analysis of archived polysomnographic datasets.
A laboratory for computational signal analysis.
None.
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.
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.
复杂性睡眠呼吸暂停被定义为继发于上气道同时阻塞和呼吸控制功能障碍的睡眠呼吸紊乱。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睡眠呼吸暂停与中枢性或复杂性睡眠呼吸暂停方面的效用。
对存档的多导睡眠图数据集进行分析。
一个用于计算信号分析的实验室。
无。
使用包含70份多导睡眠图的PhysioNet睡眠呼吸暂停数据库,这些多导睡眠图包括时长约8小时的单导联心电图信号,用于训练一种基于心电图的自主神经与呼吸相互作用测量方法(心肺耦合),以根据低频耦合升高(e-LFC)的情况检测呼吸暂停和呼吸浅慢期。在PhysioNet BIDMC充血性心力衰竭数据库(15名受试者的心电图)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谱带和窄谱带e-LFC的15条记录。后一种频谱特征似乎不仅能检测中枢性呼吸暂停期,还能检测具有周期性呼吸模式的阻塞性呼吸浅慢。将该算法应用于77项睡眠实验室分夜研究表明,窄带e-LFC的存在预示着对气道正压诱导中枢性呼吸暂停的敏感性增加。
基于心电图的频谱分析可实现对呼吸控制失调与上气道解剖性阻塞之间可能的相互作用进行自动化、独立于操作者的特征描述。光谱表型分析的临床效用,尤其是在预测气道正压治疗失败方面,仍有待更全面的测试。