# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 6 Experimental and Results
### Data Collection
#### 6.2.2. Pattern Extraction Results  
Feature extraction results from breath sounds of young children with various respiratory conditions are presented. Our previous findings indicated that LPC features exhibit similarity in the lower-order coefficients, and that LPC is not sufficiently sensitive to distinguish between different types of breath sounds [40]. Figure 6 shows the Mel-Frequency Cepstral Coefficients (MFCCs) for asthma from three different human subjects. Despite being from different children, the MFCCs under the same physical conditions exhibit notable similarity. The \(x\)-axis represents the frame index, the \(y\)-axis indicates the MFCC coefficient number (1-10), and the \(z\)-axis shows the \begin{table}
\begin{tabular}{c c c c c} \hline
**Physical Condition** & **Child ID** & **Age** & **Gender** & **Breath Segments** \\ \hline Pneumonia & 1 & 2 y & M & 18 \\  & 2 & 8 m & M & 24 \\  & 3 & 6 m & F & 19 \\ \hline Group & 4 & 18 m & M & 30 \\  & 5 & 4 m & F & 12 \\  & 6 & 4 y & F & 19 \\ \hline Asthma & 7 & 5 y & M & 16 \\  & 8 & 2 m & M & 16 \\  & 9 & 3 m & M & 12 \\ \hline Normal & 10 & 5 y & M & 29 \\  & 11 & 6 y & F & 15 \\  & 12 & 6 y & M & 26 \\ \hline \end{tabular}
\end{table} 
Table 1: Breath sound data by physical condition, child ID, age, gender, and segment count. Figure 5: Short-time energy for breath sound classification. corresponding coefficient values. First, within a single data sample, similar coefficient values can be observed across different frames at the same MFCC index. Furthermore, when comparing across different subjects, the MFCC surface plots reveal a high degree of correlation, suggesting consistency in spectral patterns associated with asthma. Figure 7 illustrates the MFCC features for various breath sound categories across different young children. For 'asthma' (upper left), a striking similarity in MFCC features is observed among the three children, indicating a consistent pattern for this condition. Similarly, in the 'croup' category (upper right), the MFCC features demonstrate notable similarity across the three breath sound files, suggesting a consistent characteristic for this diagnosis. In contrast, 'pneumonia' (lower left) exhibits a marked difference in MFCC features compared to 'asthma' and 'croup', highlighting a distinct pattern for this disease. Finally, 'normal' breath sounds (lower right) reveal a clear trend of similarity in MFCC features among the three children. These results suggest that MFCC features effectively capture the similarity within each breath sound category, supporting their potential for robust breath sound recognition. Figure 8 depicts the Dynamic Linear Predictive Coding (DLPC) features for various respiratory conditions in children, highlighting the temporal evolution of spectral patterns. For 'asthma' (upper left), DLPC features from three children show striking similarity, reflecting a consistent temporal signature for this condition. Likewise, 'croup' (upper right) reveals strong correlations among features extracted from three subjects, specifically in the first-order and high-order coefficients, indicating a unified temporal trend. 'Pneumonia' (lower left) exhibits a comparable temporal pattern. These features markedly differ from those of 'asthma' and 'croup,' underscoring DLPC's discriminative capability. Finally, 'normal' breath sounds (lower right) display consistent temporal trends and high feature similarity across three children. These results demonstrate DLPC's effectiveness in capturing temporal dynamics of breath sounds, positioning it as a robust tool for breath sound recognition. Figure 6: Mel–Frequency Cepstral Coefficients (MFCCs) extracted from breath sound recordings of three children diagnosed with asthma. Each surface plot visualizes the MFCCs with frame index values on the \(x\)-axis, coefficient numbers (1–10) on the \(y\)-axis, and coefficient values on the \(z\)-axis. Notable similarity is observed both within and across subjects, reflecting consistent spectral patterns under similar physical conditions.