title: Irregular respiration as a marker of wakefulness during titration of CPAP.
abstract: OBJECTIVE
  Regularity of respiration is characteristic of stable sleep without sleep disordered breathing. Appearance of respiratory irregularity may indicate onset of wakefulness. The present study examines whether one can detect transitions from sleep to wakefulness using only the CPAP flow signal and automate this recognition.
  
  
  METHODS
  Prospective study with blinded analysis
  
  
  METHODS
  Sleep disorder center, academic institution.
  
  
  METHODS
  74 subjects with obstructive sleep apnealhypopnea syndrome (OSAHS) INTERVENTIONS: n/a.
  
  
  RESULTS
  74 CPAP titration polysomnograms in patients with OSAHS were examined. First we visually identified characteristic patterns of ventilatory irregularity on the airflow signal and tested their relation to conventional detection of EEG defined wake or arousal. To automate recognition of sleep-wake transitions we then developed an artificial neural network (ANN) whose inputs were parameters derived exclusively from the airflow signal. This ANN was trained to identify the visually detected ventilatory irregularities. Finally, we prospectively determined the accuracy of the ANN detection of wake or arousal against EEG sleep/wake transitions. A visually identified irregular respiratory pattern (IrREG) was highly predictive of appearance of EEG wakefulness (Positive Predictive Value [PPV] = 0.89 to 0.98 across subjects). Furthermore, we were able to automate identification of this irregularity with an ANN which was highly predictive for wakefulness by EEG (PPV 0.66 to 0.86).
  
  
  CONCLUSIONS
  Despite not detecting all wakefulness, the high positive predictive value suggests that analysis of the respiration signal alone may be a useful indicator of CNS state with potential utility in the control of CPAP in OSAHS. The present study demonstrates the feasibility of automating the detection of IrREG.
authors.affiliation: Division of Pulmonary and Critical Care Medicine, NYU School of Medicine, New York, NY 10016, USA. indu.ayappa@med.nyu.edu
authors.forename: Indu
authors.initials: I
authors.lastname: Ayappa
authors.forename: Robert G
authors.initials: RG
authors.lastname: Norman
authors.forename: David
authors.initials: D
authors.lastname: Whiting
authors.forename: Albert H W
authors.initials: AH
authors.lastname: Tsai
authors.forename: Fiona
authors.initials: F
authors.lastname: Anderson
authors.forename: Emma
authors.initials: E
authors.lastname: Donnely
authors.forename: David J
authors.initials: DJ
authors.lastname: Silberstein
authors.forename: David M
authors.initials: DM
authors.lastname: Rapoport
mesh_terms: D001143:Arousal; D045422:Continuous Positive Airway Pressure; D004569:Electroencephalography; D006801:Humans; D016571:Neural Networks, Computer; D012123:Pulmonary Ventilation; D012680:Sensitivity and Specificity; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis / Q000628:therapy*; D012894:Sleep Stages; D012895:Sleep, REM; D012985:Software Design; D014851:Wakefulness