AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
Context: Respiratory morbidity is a leading cause of children’s consultations with general practitioners. Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/13/7145 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850118206267588608 |
|---|---|
| author | Lichuan Liu Wei Li Beth Moxley |
| author_facet | Lichuan Liu Wei Li Beth Moxley |
| author_sort | Lichuan Liu |
| collection | DOAJ |
| description | Context: Respiratory morbidity is a leading cause of children’s consultations with general practitioners. Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experience to identify subtle differences in children’s breath sounds. Furthermore, obtaining reliable feedback from young children about their physical condition is challenging. Methods: The use of a human–artificial intelligence (AI) tool is an essential component for screening and monitoring young children’s respiratory diseases. Using clinical data to design and validate the proposed approaches, we propose novel methods for recognizing and classifying children’s breath sounds. Different breath sound signals were analyzed in the time domain, frequency domain, and using spectrogram representations. Breath sound detection and segmentation were performed using digital signal processing techniques. Multiple features—including Mel–Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), spectral entropy, and Dynamic Linear Prediction Coefficients (DLPCs)—were extracted to capture both time and frequency characteristics. These features were then fed into various classifiers, including K-Nearest Neighbor (KNN), artificial neural networks (ANNs), hidden Markov models (HMMs), logistic regression, and decision trees, for recognition and classification. Main Findings: Experimental results from across 120 infants and preschoolers (2 months to 6 years) with respiratory disease (30 asthma, 30 croup, 30 pneumonia, and 30 normal) verified the performance of the proposed approaches. Conclusions: The proposed AI system provides a real-time diagnostic platform to improve clinical respiratory management and outcomes in young children, thereby reducing healthcare costs. Future work exploring additional respiratory diseases is warranted. |
| format | Article |
| id | doaj-art-0f20d2ab3ba647f1968f64613f0a35b9 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0f20d2ab3ba647f1968f64613f0a35b92025-08-20T02:35:56ZengMDPI AGApplied Sciences2076-34172025-06-011513714510.3390/app15137145AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory ScreeningLichuan Liu0Wei Li1Beth Moxley2Department of Electrical Engineering, Northern Illinois University, DeKalb, IL 60115, USAAlexander Innovation Centre, Vancouver, BC V6B 1Y1, CanadaSchool of Nursing, Northern Illinois University, DeKalb, IL 60115, USAContext: Respiratory morbidity is a leading cause of children’s consultations with general practitioners. Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experience to identify subtle differences in children’s breath sounds. Furthermore, obtaining reliable feedback from young children about their physical condition is challenging. Methods: The use of a human–artificial intelligence (AI) tool is an essential component for screening and monitoring young children’s respiratory diseases. Using clinical data to design and validate the proposed approaches, we propose novel methods for recognizing and classifying children’s breath sounds. Different breath sound signals were analyzed in the time domain, frequency domain, and using spectrogram representations. Breath sound detection and segmentation were performed using digital signal processing techniques. Multiple features—including Mel–Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), spectral entropy, and Dynamic Linear Prediction Coefficients (DLPCs)—were extracted to capture both time and frequency characteristics. These features were then fed into various classifiers, including K-Nearest Neighbor (KNN), artificial neural networks (ANNs), hidden Markov models (HMMs), logistic regression, and decision trees, for recognition and classification. Main Findings: Experimental results from across 120 infants and preschoolers (2 months to 6 years) with respiratory disease (30 asthma, 30 croup, 30 pneumonia, and 30 normal) verified the performance of the proposed approaches. Conclusions: The proposed AI system provides a real-time diagnostic platform to improve clinical respiratory management and outcomes in young children, thereby reducing healthcare costs. Future work exploring additional respiratory diseases is warranted.https://www.mdpi.com/2076-3417/15/13/7145breath sound classificationrespiratory health monitoringdigital signal processingfeature extractionmachine learning classifiersperformance assessment |
| spellingShingle | Lichuan Liu Wei Li Beth Moxley AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening Applied Sciences breath sound classification respiratory health monitoring digital signal processing feature extraction machine learning classifiers performance assessment |
| title | AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening |
| title_full | AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening |
| title_fullStr | AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening |
| title_full_unstemmed | AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening |
| title_short | AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening |
| title_sort | ai based classification of pediatric breath sounds toward a tool for early respiratory screening |
| topic | breath sound classification respiratory health monitoring digital signal processing feature extraction machine learning classifiers performance assessment |
| url | https://www.mdpi.com/2076-3417/15/13/7145 |
| work_keys_str_mv | AT lichuanliu aibasedclassificationofpediatricbreathsoundstowardatoolforearlyrespiratoryscreening AT weili aibasedclassificationofpediatricbreathsoundstowardatoolforearlyrespiratoryscreening AT bethmoxley aibasedclassificationofpediatricbreathsoundstowardatoolforearlyrespiratoryscreening |