Plamen Bokov1, Bruno Mahut2, Patrice Flaud3, Christophe Delclaux4. 1. 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. 2. Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Physiologie - Clinique de la Dyspnée, Paris, France. 3. Laboratoire Matière et Systèmes Complexes, UMR 7057, Université Paris Diderot, Paris, France. 4. 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.
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. METHOD: 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.
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. METHOD: 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: Juan De La Torre Cruz; Francisco Jesús Cañadas Quesada; Nicolás Ruiz Reyes; Pedro Vera Candeas; Julio José Carabias Orti Journal: Sensors (Basel) Date: 2020-05-08 Impact factor: 3.576