title: A Vision-Based Respiration Monitoring System for Passive Airway Resistance Estimation.
abstract: Airway resistance is the mechanical cause of most of the symptoms in obstructive pulmonary disease, and can be considered as the primary measure of disease severity. A low-cost and noninvasive method to measure the airway resistance that does not require patient effort could be of great benefit in evaluating the severity of lung diseases, especially in patient population that are unable to use spirometry, such as young children.
  
  
  
  The Vision-Based Passive Airway Resistance Estimation (VB-PARE) technology is a passive method to measure airway resistance noninvasively. The airway resistance is estimated from: 1) airflow extracted from processing depth data captured by a Microsoft Kinect, and 2) Pulsus Paradoxus extracted from a pulse oximeter (SpO2).
  
  
  
  To verify the validity and accuracy of the VB-PARE, two phases of experiment were conducted. In Phase I, spontaneous breathing data was collected from 14 healthy participants with externally induced airway obstruction, and the accuracy of 76.2±13.8% was achieved in predicting three levels of obstruction severity. In Phase II, VB-PARE outputs were compared with the clinical results from 14 patients. VB-PARE estimated the tidal volume with an average error of 0.07±0.06 liter. Also, patients with airway obstruction were detected with 80% accuracy.
  
  
  
  Using the information extracted from Kinect and SpO2, here, we present a quantitative method to measure the severity of airway obstruction without requiring active patient involvement.
  
  
  
  The proposed VB-PARE system contributes to the state-of-art respiration monitoring methods by expanding the idea of passive and noninvasive airway resistance measurement.
authors.forename: Sarah
authors.initials: S
authors.lastname: Ostadabbas
authors.forename: Nordine
authors.initials: N
authors.lastname: Sebkhi
authors.forename: Mingxi
authors.initials: M
authors.lastname: Zhang
authors.forename: Salman
authors.initials: S
authors.lastname: Rahim
authors.forename: Larry J
authors.initials: LJ
authors.lastname: Anderson
authors.forename: Frances Eun-Hyung
authors.initials: FE
authors.lastname: Lee
authors.forename: Maysam
authors.initials: M
authors.lastname: Ghovanloo
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000402:Airway Obstruction / Q000175:diagnosis* / Q000503:physiopathology*; D000403:Airway Resistance / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017156:Photoplethysmography / Q000295:instrumentation* / Q000379:methods; D015203:Reproducibility of Results; D012129:Respiratory Function Tests / Q000295:instrumentation* / Q000379:methods; D056152:Respiratory Rate; D012680:Sensitivity and Specificity; D014584:User-Computer Interface