Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population. (1st March 2016)

• Record Type: Journal Article
• Authors: Bokov, Plamen; Mahut, Bruno; Flaud, Patrice; Delclaux, Christophe
• Is Part Of: Computers in biology and medicine. Volume 70(2016)
• Page Start: 40
• Page End: 50
• Publication Date: 2016-03-01
• Subjects: Automated wheezing detection -- Childhood asthma -- Bronchiolitis -- Support vector machine -- ROC analysis; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.01.002
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: We recorded by Smartphone respiratory sounds at the mouth in pediatric population. Two clinical operators validated the presence or absence of wheezing in 97 toddlers. We used Short-Time Fourier Transform and SVM classifier for wheeze recognition. 71.4% Sensitivity and 88.9% Specificity were observed for wheeze detection. An independent test found a fair agreement with a clinical operator.