title: Sleep apnea diagnosis using an ECG Holter device including a nasal pressure (NP) recording: validation of visual and automatic analysis of nasal pressure versus full polysomnography.
abstract: BACKGROUND
  New simplified techniques for diagnosing sleep apnea should be specially tailored for easy use in cardiologic practice.
  
  
  OBJECTIVE
  e dedicated one of the channels of a Holter Electrocardiogram (ECG) device (SpiderView() ELA Medical, France) to nasal pressure (NP) recordings. We also developed an automatic analysis of NP signal providing an apnea-hypopnea index (AHI) for physicians without the know-how in sleep medicine.
  
  
  METHODS
  Thirty-four unselected patients referred for symptoms suggesting sleep apnea underwent a polysomnography (PSG) with simultaneous NP and Holter ECG recordings. An expert blinded to PSG results visually scored the Holter plus NP recordings. The results of the AHI obtained in PSG (AHI-PSG) were compared, respectively, to the AHI-NP obtained by visual analysis and automatic analysis (AHI-NP Auto) of Holter ECG nasal pressure.
  
  
  RESULTS
  In 10 randomly selected subjects (development set), the best cut-off on Holter ECG for diagnosing sleep apnea patients as defined by AHI>20/h in PSG was determined at 35 events/h by a receiver operator curve (ROC) analysis. Prospective testing of this threshold was then performed in 19 subjects (test set). For visually scored recordings of Holter ECG plus NP, we obtained a negative predictive value (NPV) of 80% and a positive predictive value (PPV) of 100% for sleep apnea. The area under the ROC curve was 0.97. For the automatic analysis, the NPV was 86% and the PPV value 100%. The area under the ROC curve was 0.85.
  
  
  CONCLUSIONS
  NP recording using a Holter system is an efficient and easy-to-use tool for screening for sleep-disordered breathing in routine cardiology practice.
authors.affiliation: Cardiology Department, EFCR et Laboratoire du sommeil, Pôle Rééducation-Physiologie, CHU de Grenoble, BP 217X-38043 Grenoble Cedex 09, France. JPepin@chu-grenoble.fr
authors.forename: Jean-Louis
authors.initials: JL
authors.lastname: Pépin
authors.forename: Pascal
authors.initials: P
authors.lastname: Defaye
authors.forename: Elodie
authors.initials: E
authors.lastname: Vincent
authors.forename: Sylvain
authors.initials: S
authors.lastname: Christophle-Boulard
authors.forename: Renaud
authors.initials: R
authors.lastname: Tamisier
authors.forename: Patrick
authors.initials: P
authors.lastname: Lévy
mesh_terms: D000328:Adult; D000465:Algorithms; D001331:Automation; D015716:Electrocardiography, Ambulatory / Q000295:instrumentation* / Q000592:standards; D005260:Female; D006801:Humans; D008297:Male; D008365:Manometry / Q000295:instrumentation* / Q000592:standards; D008875:Middle Aged; D009296:Nasal Cavity / Q000503:physiopathology*; D017286:Polysomnography / Q000592:standards; D011237:Predictive Value of Tests; D011312:Pressure; D011446:Prospective Studies; D016037:Single-Blind Method; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology*