Envelope Analysis of the Airflow Signal To Improve Polysomnographic Assessment of Sleep Disordered Breathing

Javier A. Díaz, José M. Arancibia, Alejandro Bassi, Ennio A. Vivaldi
2014 Sleep  
199 Envelope Analysis for Sleep-Disordered Breathing-Díaz et al Given the drastic impact on morbidity and mortality, 1-9 increasing prevalence, 3,10-13 and efficient treatability of sleep disordered breathing (SDB), 3,14-16 improving its diagnostic procedures is a relevant health goal. SDB includes obstructive sleep apnea (OSA) 17 and upper airway resistance syndrome (UARS). 17,18 Currently, both diagnoses are assessed through polysomnography by detecting and counting abnormal respiratory
more » ... such as apneas, hypopneas and respiratory effort-related arousals (RERAs). The apnea-hypopnea index (AHI) 17,19,20 is considered the standard measure for case identification, severity quantification, and prevalence assessment. 17, 19, 21 Nonetheless, defining respiratory events has remained a controversial subject, particularly with hypopneas. The search for event thresholds that differentiate normal from abnormal breathing in a physiologically and clinically meaningful manner entails several issues. Firstly, signals obtained from standard PSG airflow sensors, including nasal cannula pressure transducers and thermistors, 19 are uncalibrated and prone to scale fluctuations, yielding nonlinear relationships with pneumotachography data, the gold standard for airflow Study Objectives: 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. Design: Application of an algorithm for envelope analysis to standard nasal cannula signal from actual polysomnographic studies. Setting: PSG recordings from a sleep disorders center were analyzed by an algorithm developed on the Igor scientific data analysis software. Patients or Participants: Recordings representative of different degrees of sleep disordered breathing (SDB) severity or illustrative of the covariation between breathing and particularly relevant factors and variables. Interventions: 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. Measurements and Results: 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. Conclusions: 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. Keywords: Signal envelope analysis, polysomnography, nasal cannula / pressure, transducer, medical informatics applications, upper airway resistance sleep apnea syndrome, sleep apnea syndromes Citation: Díaz JA; Arancibia JM; Bassi A; Vivaldi EA. Envelope analysis of the airflow signal to improve polysomnographic assessment of sleep disordered breathing. SLEEP 2014;37(1):199-208.
doi:10.5665/sleep.3338 pmid:24470709 pmcid:PMC3902884 fatcat:ei4bkc4kdbdgngdvpnmjgv57f4