Penzel Thomas, Kantelhardt Jan W, Grote Ludger, Peter Jörg-Hermann, Bunde Armin
Division of Pulmonary Diseases, Department of Internal Medicine, Hospital of Philipps-University, Baldingerstrasse 1, D-35033 Marburg, Germany.
IEEE Trans Biomed Eng. 2003 Oct;50(10):1143-51. doi: 10.1109/TBME.2003.817636.
Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.
睡眠一直被视为自主神经系统的一种测试情境，因为其活动受睡眠阶段的调节。与睡眠相关的呼吸障碍也会影响自主神经系统，并可导致被称为周期性变化的心率改变。我们通过分析心率变异性（HRV）来研究睡眠阶段和睡眠呼吸暂停对自主活动的影响。由于频谱分析适用于识别周期性变化，而去趋势波动分析可以分析标度行为并检测长程相关性，我们在14名健康受试者、33名中度睡眠呼吸暂停患者和31名重度睡眠呼吸暂停患者中比较了这两种互补技术的结果。频谱参数VLF、LF、HF和LF/HF证实，从清醒和快速眼动睡眠到浅睡眠再到深睡眠，副交感神经活动逐渐增强，而睡眠呼吸暂停患者的这种活动则有所减弱。基于个体和睡眠阶段进行判别分析，以确定区分睡眠阶段和睡眠呼吸暂停严重程度的最佳方法。使用频谱参数时，呼吸暂停严重程度分类的正确率为69.7%，睡眠阶段分类的正确率为54.6%，而使用标度分析时，这些数字分别提高到了74.4%和85.0%。我们得出结论，与频谱分析相比，通过标度分析能更好地量化HRV的变化。
