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A wheeze recognition algorithm for practical implementation in children
Chizu Habukawa1, Naoto Ohgami2, Naoki Matsumoto3
1Department of Paediatrics, Minami Wakayama Medical Center, Wakayama, Japan.
Insights
A new algorithm accurately detects wheezes in children, aiding respiratory illness management. This automated wheeze detection shows high sensitivity and specificity, proving useful for home-based monitoring devices.
Area of Science:
- Pediatric Pulmonology
- Medical Device Technology
- Computational Health
Background:
- Wheeze detection is crucial for managing respiratory diseases in children.
- Current clinical methods for automatic wheeze detection in children lack high accuracy.
- There is a need for practical, automated solutions for wheeze detection in pediatric respiratory care.
Purpose of the Study:
- To develop and evaluate a novel algorithm for automatic wheeze detection in children.
- To assess the algorithm's accuracy and reliability compared to specialist physician assessments.
- To determine the algorithm's suitability for practical implementation in pediatric respiratory illness management.
Main Methods:
- Developed a wheeze recognition algorithm based on established Computerized Respiratory Sound Analysis guidelines.
- Recorded 30-second lung sound samples from 214 children (2 months to 12 years) in a pediatric setting.
- Compared algorithm's wheeze detection performance against consensus diagnoses from two specialist physicians, calculating sensitivity, specificity, PPV, and NPV.
Main Results:
- The wheeze recognition algorithm demonstrated high performance across all tested sound files.
- Achieved sensitivity of 100%, specificity of 95.7%, positive predictive value (PPV) of 90.3%, and negative predictive value (NPV) of 100%.
- Age did not significantly influence the algorithm's wheeze detection sensitivity.
Conclusions:
- The developed wheeze recognition algorithm effectively differentiates wheezes from noise in pediatric lung sound recordings.
- The algorithm's high accuracy suggests its potential utility in home-based respiratory illness management devices.
- This technology could enhance remote monitoring and early intervention for respiratory conditions in children.
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.
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