Author ORCID Identifier
Elizabeth Moxley: https://orcid.org/0000-000200929-4717
Lichuan Liu: https://orcid.org/0009-0009-2005-6902
Document Type
Article
Publication Title
Applied Sciences
Abstract
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.
DOI
10.3390/app15137145
Publication Date
7-1-2025
Recommended Citation
Liu, L.; Li, W.; Moxley, B. AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening. Appl. Sci. 2025, 15, 7145. https://doi.org/10.3390/app15137145
Original Citation
Liu, L.; Li, W.; Moxley, B. AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening. Appl. Sci. 2025, 15, 7145. https://doi.org/10.3390/app15137145
Department
Department of Electrical Engineering| School of Nursing
