# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 1 Introduction
However, most existing studies focus either on distinguishing normal from abnormal breath sounds--such as wheezing or crackles--or single respiratory conditions like asthma. There is limited research targeting disease-specific classification across a broader range of pediatric respiratory diseases, such as group or pneumonia. Breath sounds, characterized as non-stationary and time-varying, pose significant challenges to traditional speech signal processing techniques, necessitating tailored adaptations for effective analysis. In young children, unique anatomical and developmental differences produce breath sounds with distinct individual traits that deviate from standard speech signal models. Consequently, applying fixed-parameter LPCs or MFCCs to pediatric breath sounds proves suboptimal. This paper introduces a novel method leveraging Dynamic Linear Prediction Coefficients (DLPCs) for breath sound recognition. Unlikeconventional LPCs, DLPCs treat coefficients as time-dependent functions rather than static values, adeptly capturing evolving frequency patterns in non-stationary signals. This approach enables analysis of extended signal segments, lowering computational demands while preserving flexibility across diverse subjects. Moreover, in order to capture the nonlinear chaotic dynamics of disordered breathing, such as airflow turbulence or airway obstruction, which are critical indicators of respiratory pathologies in pediatric populations, and enable robust classification of normal versus abnormal breath sounds, this paper employs a spectral entropy approach as another complementary feature [37; 38; 39]. Spectral entropy quantifies the complexity and irregularity of acoustic signals by computing the Shannon entropy of the normalized power spectral density across breath sound segments. This approach allows us to detect subtle physiological changes, differentiate distinct pathologies, and maintain resilience against ambient noise. Building on previous work [40; 41], the proposed methods advance breath sound analysis by incorporating information from the time, frequency, and spectrogram domains. The approach is guided and validated by input from health professionals. Two distinct features--Dynamic Linear Prediction Coefficients (DLPCs) and spectral entropy--are introduced to capture dynamic acoustic patterns with greater accuracy. These features are evaluated using random forest and logistic regression classifiers to assess their respective strengths and limitations. This comparative analysis helps to identify the most effective feature representations for future integration or optimization, and it provides a robust framework for pediatric breath sound classification. Section 2 examines respiratory sounds and respiratory diseases in young children and their characteristics, followed by an acoustic analysis using signal processing techniques. Section 3 discusses breath sound preprocessing techniques and detection methods. Section 4 introduces pattern extraction methods, including commonly used speech signal features--Linear Predictive Coding (LPC), Linear Predictive Cepstral Coefficients (LPCs), and Mel-Frequency Cepstral Coefficients (MFCCs)--as well as newly proposed features for breath sound analysis, such as dynamic LPC and spectral entropy. Section 5 explores breath sound recognition using the K-Nearest Neighbor (KNN), hidden Markov model (HMM), artificial neural network (ANN), logistic regression, and decision tree classifiers. Sections 6 and 7 present and discuss the experimental results, while Section 8 summarizes the findings and discusses future directions.