Laboratorio de Sueño y Cronobiología, Programa de Fisiología y Biofísica, Instituto de Ciencias Biomédicas (ICBM), Facultad de Medicina, Universidad de Chile, Santiago, Chile.
Sleep. 2014 Jan 1;37(1):199-208. doi: 10.5665/sleep.3338.
Given the detailed respiratory waveform signal provided by the nasal cannula in polysomnographic (PSG) studies, to quantify sleep breathing disturbances by extracting a continuous variable based on the coefficient of variation of the envelope of that signal.
Application of an algorithm for envelope analysis to standard nasal cannula signal from actual polysomnographic studies.
PSG recordings from a sleep disorders center were analyzed by an algorithm developed on the Igor scientific data analysis software.
Recordings representative of different degrees of sleep disordered breathing (SDB) severity or illustrative of the covariation between breathing and particularly relevant factors and variables.
The method calculated the coefficient of variation of the envelope for each 30-second epoch. The normalized version of that coefficient was defined as the respiratory disturbance variable (RDV). The method outcome was the all-night set of RDV values represented as a time series.
RDV quantitatively reflected departure from normal sinusoidal breathing at each epoch, providing an intensity scale for disordered breathing. RDV dynamics configured itself in recognizable patterns for the airflow limitation (e.g., in UARS) and the apnea/hypopnea regimes. RDV reliably highlighted clinically meaningful associations with staging, body position, oximetry, or CPAP titration.
Respiratory disturbance variable can assess sleep breathing disturbances as a gradual phenomenon while providing a comprehensible and detailed representation of its dynamics. It may thus improve clinical diagnosis and provide a revealing descriptive tool for mechanistic sleep disordered breathing modeling. Respiratory disturbance variable may contribute to attaining simplified screening methodologies, novel diagnostic criteria, and insightful research tools.
鉴于多导睡眠图（PSG）研究中鼻插管提供的详细呼吸波信号，通过提取基于该信号包络变化系数的连续变量来量化睡眠呼吸障碍。
将一种用于包络分析的算法应用于实际多导睡眠图研究中的标准鼻插管信号。
通过在 Igor 科学数据分析软件上开发的算法分析来自睡眠障碍中心的 PSG 记录。
记录代表不同程度睡眠呼吸障碍（SDB）严重程度的代表性记录，或说明呼吸与特别是相关因素和变量之间的共变关系。
该方法计算每个 30 秒时段包络的变化系数。该系数的归一化版本被定义为呼吸干扰变量（RDV）。该方法的结果是以时间序列表示的整夜 RDV 值集。
RDV 定量反映了每个时段正常正弦呼吸的偏离，为呼吸障碍提供了一个强度尺度。RDV 动力学为气流受限（例如 UARS 中）和呼吸暂停/低通气综合征的模式配置了自身。RDV 可靠地突出了与分期、体位、血氧饱和度或 CPAP 滴定的临床有意义的关联。
呼吸干扰变量可以评估睡眠呼吸障碍作为一个渐进的现象，同时提供其动力学的可理解和详细的表示。因此，它可以改善临床诊断，并为睡眠呼吸障碍的机制建模提供一个有启发性的描述性工具。呼吸干扰变量可能有助于实现简化的筛选方法、新的诊断标准和有见地的研究工具。
