Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population. (1st March 2016) - Bokov, Plamen; Mahut, Bruno; Flaud, Patrice; Delclaux, Christophe
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.