title: Feasibility of automated detection of advanced sleep disordered breathing utilizing an implantable pacemaker ventilation sensor.
abstract: OBJECTIVE
  This study tested the feasibility of automatically detecting advanced sleep disordered breathing (SDB) from a pacemaker trans-thoracic impedance sensor.
  
  
  BACKGROUND
  SDB is prevalent yet under-diagnosed in patients with cardiovascular disease. The potential for automated detection of SDB in patients receiving pacemakers with respiration sensors has not been fully explored. We hypothesized that the trans-thoracic impedance sensor could be utilized for automatic detection of advanced SDB.
  
  
  METHODS
  Patients underwent overnight polysomnography (PSG). The pacemaker trans-thoracic impedance signal was simultaneously recorded and time synchronized with the polysomnograph. Cardiovascular health variables were abstracted from medical records. Apnea was defined as cessation of inspiratory airflow lasting 10 seconds or longer. Hypopnea was defined as a reduction of tidal volume of at least 30% from baseline tidal volume, lasting 10 seconds or more. A computer algorithm (PM-A) was developed to automatically detect SDB from the pacemaker impedance sensor data. The performance of automated SDB detection was compared against PSG.
  
  
  RESULTS
  Sixty patients (aged 69 +/- 12 years, 45 males) were studied. Advanced SDB (moderate or severe) was diagnosed in 40 patients. Severe SDB (apnea-hypopnea index [AHI]> or = 30) was diagnosed in 32 patients (53%), but only 5 patients had prior diagnosis of the disease. Moderate SDB (30 > AHI > 15) was diagnosed in 8 patients of whom only two were previously diagnosed. Cardiovascular health variables did not predict the presence of advanced SDB. PM-A derived AHI correlated with that of the PSG (r = 0.80, P < 0.01). The algorithm identified patients with advanced SDB with 82% sensitivity and 88% specificity.
  
  
  CONCLUSIONS
  It is feasible to automatically measure SDB severity using a pacemaker trans-thoracic impedance sensor. Advanced SDB was frequently undiagnosed in this cohort of pacemaker patients.
authors.affiliation: Division of Cardiology, Pittsburgh VA Healthcare System, University of Pittsburgh, Pittsburgh, PA 15240, USA. Alaa.Shalaby@med.va.gov
authors.forename: Alaa
authors.initials: A
authors.lastname: Shalaby
authors.forename: Charles
authors.initials: C
authors.lastname: Atwood
authors.forename: Claudius
authors.initials: C
authors.lastname: Hansen
authors.forename: Martin
authors.initials: M
authors.lastname: Konermann
authors.forename: Pradip
authors.initials: P
authors.lastname: Jamnadas
authors.forename: Kent
authors.initials: K
authors.lastname: Lee
authors.forename: Roger
authors.initials: R
authors.lastname: Willems
authors.forename: Jesse
authors.initials: J
authors.lastname: Hartley
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Stahmann
authors.forename: Jonathan
authors.initials: J
authors.lastname: Kwok
authors.forename: Quan
authors.initials: Q
authors.lastname: Ni
authors.forename: Joerg
authors.initials: J
authors.lastname: Neuzner
mesh_terms: D000368:Aged; D000465:Algorithms; D002307:Cardiography, Impedance; D018450:Disease Progression; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010138:Pacemaker, Artificial; D012680:Sensitivity and Specificity; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology