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Time-Variant Spectral Analysis of the Heart Rate Variability during Sleep in Healthy and Obstructive Sleep Apnoea Subjects

机译:健康梗塞睡眠呼吸暂停睡眠中睡眠期间心率变异的时变频分析

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A time-variant autoregressive approach was used in order to evaluate the spectral parameters of the heart rate variability (HRV), through the all sleep stages, in both normal subjects and patients with severe obstructive sleep apnoea. Recordings coming from five normal and five pathologic subjects were analyzed in the study. The parameters of the autoregressive model were fixed for the entire night recordings: model order = 8 and forgetting factor = 0.98. The classical spectral indexes of the Heart Rate Variability were normalized respect to the total power. The results in this study showed, in normal subjects, an increment in the HFn components in NREM, while pathologic subjects presented a reduced activity in this component in all the time, which suggest a low activation of the vagal nerve. Furthermore, in both groups of subjects, VLFn reached high levels during REM and wake than NREM. In conclusion, this method could offer an alternative approach, with high resolution and efficient computation in the spectral decompotition, in order to develop a classification of sleep stage and apnoea detectors from the HRV.
机译:使用时变自自回归方法,以评估心率变异性(HRV)的光谱参数,通过所有睡眠阶段,在正常受试者和严重阻塞性睡眠呼吸暂停的患者中。在研究中分析了来自五个正常和五个病理受试者的录音。自回归模型的参数固定为整个夜间录制:型号顺序= 8,忘记因子= 0.98。心率变异性的经典光谱索引被标准化为总功率。该研究的结果显示,在正常受试者中,NREM中的HFN组分中的增量,而病理受试者在该组分中介绍了该组分的减少,这表明迷走神经的较低激活。此外,在两组受试者中,VLFN在REM和唤醒时达到高水平,而不是NREM。总之,该方法可以提供一种替代方法,具有高分辨率和高效的频谱分解,以便从HRV开发睡眠阶段和呼吸暂停检测器的分类。

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