title: Hilbert-Huang Transform Yields Improved Minute Volume Estimates from Respiratory Inductance Plethysmography During Transitions to Paradoxical Breathing.
abstract: BACKGROUND
  Anesthesia and sedation are associated with paradoxical breathing. Respiratory inductance plethysmography (RIP) permits measurement of respiratory motion in clinical settings not conducive to spirometry, but correlation of RIP volume changes and spirometer flow in the time domain is degraded by the development of paradoxical breathing. The Hilbert-Huang transform (HHT) is a nonlinear signal analysis method that permits the instantaneous magnitude and phase of nonstationary signals to be estimated in the frequency domain. We hypothesized that these frequency domain estimates would provide higher correlation between RIP and spirometer signals than time domain signals during the transition between normal and paradoxical breathing.
  
  
  METHODS
  From 51 patients undergoing sevoflurane anesthesia for minor procedures, a 5-minute epoch containing transitions between pressure support ventilation and spontaneous ventilation was selected for analysis. Pearson correlation for models based on HHT magnitude and phase was compared with models based on time domain signals. Bland-Altman analysis was performed to assess deviation from linearity in the models.
  
  
  RESULTS
  For the 51 patients analyzed, the modulation of tidal volume over the epoch ranged from 30% to 215% of epoch mean. The coefficient of determination for time domain analysis was 0.62 ± 0.2 compared with 0.93 ± 0.07 for the HHT model incorporating phase. This improvement of 0.31 (99% confidence interval, 0.24-0.37) was significant (P < 0.0001). No trend was observed in prediction residuals.
  
  
  CONCLUSIONS
  Under conditions of changing ventilation, HHT-derived magnitude and phase measures provide higher correlation with spirometry than those obtained with traditional time domain methods.
authors.affiliation: From the Department of Anesthesiology & Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Jeff E
authors.initials: JE
authors.lastname: Mandel
authors.forename: Joshua H
authors.initials: JH
authors.lastname: Atkins
mesh_terms: D000769:Anesthesia, Inhalation; D018685:Anesthetics, Inhalation / Q000008:administration & dosage*; D006801:Humans; D016014:Linear Models; D008168:Lung / Q000187:drug effects* / Q000502:physiology; D008738:Methyl Ethers / Q000008:administration & dosage*; D016343:Monitoring, Intraoperative / Q000379:methods*; D017711:Nonlinear Dynamics; D010991:Plethysmography; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012119:Respiration / Q000187:drug effects*; D012121:Respiration, Artificial; D000077149:Sevoflurane; D012815:Signal Processing, Computer-Assisted; D013147:Spirometry; D013990:Tidal Volume; D013997:Time Factors
chemical_list: D018685:Anesthetics, Inhalation; D008738:Methyl Ethers; D000077149:Sevoflurane