title: Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population.
abstract: BACKGROUND
  Respiratory diseases in children are a common reason for physician visits. A diagnostic difficulty arises when parents hear wheezing that is no longer present during the medical consultation. Thus, an outpatient objective tool for recognition of wheezing is of clinical value.
  
  
  METHODS
  We developed a wheezing recognition algorithm from recorded respiratory sounds with a Smartphone placed near the mouth. A total of 186 recordings were obtained in a pediatric emergency department, mostly in toddlers (mean age 20 months). After exclusion of recordings with artefacts and those with a single clinical operator auscultation, 95 recordings with the agreement of two operators on auscultation diagnosis (27 with wheezing and 68 without) were subjected to a two phase algorithm (signal analysis and pattern classifier using machine learning algorithms) to classify records.
  
  
  RESULTS
  The best performance (71.4% sensitivity and 88.9% specificity) was observed with a Support Vector Machine-based algorithm. We further tested the algorithm over a set of 39 recordings having a single operator and found a fair agreement (kappa=0.28, CI95% [0.12, 0.45]) between the algorithm and the operator.
  
  
  CONCLUSIONS
  The main advantage of such an algorithm is its use in contact-free sound recording, thus valuable in the pediatric population.
authors.affiliation: Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Physiologie - Clinique de la Dyspnée, Paris, France; Université Paris Descartes, Paris Sorbonne Cité, Paris, France. Electronic address: plamen.bokov@aphp.fr.
authors.forename: Plamen
authors.initials: P
authors.lastname: Bokov
authors.affiliation: Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Physiologie - Clinique de la Dyspnée, Paris, France.
authors.forename: Bruno
authors.initials: B
authors.lastname: Mahut
authors.affiliation: Laboratoire Matière et Systèmes Complexes, UMR 7057, Université Paris Diderot, Paris, France.
authors.forename: Patrice
authors.initials: P
authors.lastname: Flaud
authors.affiliation: Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Physiologie - Clinique de la Dyspnée, Paris, France; Université Paris Descartes, Paris Sorbonne Cité, Paris, France; CIC Plurithématique 9201, Hôpital Européen Georges Pompidou, Paris, France.
authors.forename: Christophe
authors.initials: C
authors.lastname: Delclaux
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D012135:Respiratory Sounds; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
keywords: Automated wheezing detection; Bronchiolitis; Childhood asthma; ROC analysis; Support vector machine