Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population
Plamen Bokov1, Bruno Mahut2, Patrice Flaud3
1Assistance 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.
Insights
A new smartphone algorithm can detect wheezing in children, aiding diagnosis when symptoms are intermittent. This objective tool offers valuable outpatient recognition of respiratory sounds.
Area of Science:
- Pediatric Pulmonology
- Computational Health
- Biomedical Signal Processing
Background:
- Respiratory diseases are common in children, often presenting with wheezing.
- Diagnosing intermittent wheezing can be challenging in clinical settings.
- An objective outpatient tool for wheezing recognition is clinically valuable.
Purpose of the Study:
- To develop and evaluate a smartphone-based algorithm for objective wheezing recognition in pediatric respiratory sounds.
- To assess the algorithm's performance using machine learning techniques.
Main Methods:
- A two-phase algorithm was developed, involving signal analysis and machine learning (Support Vector Machine).
- Recordings of respiratory sounds were collected from 95 pediatric patients (27 with wheezing, 68 without).
- Algorithm performance was evaluated using sensitivity and specificity metrics.
Main Results:
- The Support Vector Machine algorithm achieved 71.4% sensitivity and 88.9% specificity.
- A fair agreement (kappa=0.28) was found between the algorithm and single operator auscultation on a separate set of recordings.
Conclusions:
- The developed algorithm provides an objective method for wheezing recognition in children.
- Contact-free sound recording via smartphone offers a valuable, non-invasive tool for pediatric respiratory assessment.
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.
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