A wheeze recognition algorithm for practical implementation in children

PLoS One. 2020 Oct 8;15(10):e0240048. doi: 10.1371/journal.pone.0240048. eCollection 2020.

Abstract

Background: The detection of wheezes as an exacerbation sign is important in certain respiratory diseases. However, few highly accurate clinical methods are available for automatic detection of wheezes in children. This study aimed to develop a wheeze detection algorithm for practical implementation in children.

Methods: A wheeze recognition algorithm was developed based on wheezes features following the Computerized Respiratory Sound Analysis guidelines. Wheezes can be detected by auscultation with a stethoscope and using an automatic computerized lung sound analysis. Lung sounds were recorded for 30 s in 214 children aged 2 months to 12 years and 11 months in a pediatric consultation room. Files containing recorded lung sounds were assessed by two specialist physicians and divided into two groups: 65 were designated as "wheeze" files, and 149 were designated as "no-wheeze" files. All lung sound judgments were agreed between two specialist physicians. We compared wheeze recognition between the specialist physicians and using the wheeze recognition algorithm and calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files to evaluate the influence of age on the wheeze detection sensitivity.

Results: The detection of wheezes was not influenced by age. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 100%, 95.7%, 90.3%, and 100%, respectively.

Conclusions: The wheeze recognition algorithm could identify wheezes in sound files and therefore may be useful in the practical implementation of respiratory illness management at home using properly developed devices.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Auscultation
  • Child
  • Child, Preschool
  • Diagnosis, Computer-Assisted / methods
  • Female
  • Humans
  • Infant
  • Lung Diseases / diagnosis*
  • Male
  • Respiratory Sounds / physiology*
  • Sensitivity and Specificity

Grants and funding

The Omron Health Care Corporation provided support in the form of salaries for authors Naoto Ohgami, Naoki Matsumoto, Kenji Hashino, Kei Asai and Tetsuya Sato, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the “author contributions” section. This does not alter our adherence to the policies of PlosOne on sharing data and materials. Chizu Habukawa and Katsumi Murakami received a research grant from Omron Health Care Corporation.