A new lung disorder detection model based on graphene pattern using respiratory sounds

Background and purpose: Auscultatory sounds acquired using a stethoscope can offer clinical clues to the presence of cardiorespiratory diseases. In this work, we aimed to develop an accurate and lightweight model for disease detection using lung sounds. Method: Our model comprises: (1) signal decomposition using a multilevel bidirectional wavelet transformation; (2) multilevel feature generation using a novel lattice-based graphene pattern to create minimum- and maximum directed graphs to extract textural features; (3) feature selection using iterative neighborhood component analysis; (4) classification using a standard shallow k-nearest neighbor function. We tested the model on a public 336-subject eight-class lung sound dataset. Model performance was reported for eight- and three-class diagnostic classification. Results: Our model achieved accuracy rates exceeding 94 % for all classification tasks. The maximum distance path through the graphene pattern consistently outperformed the minimum distance path, indicating that sig- nificant amplitude transitions in respiratory sounds contain more discriminative information than regions of relative uniformity. Elements of the input signal and wavelet decomposition bands that contributed most to the selected feature vector were visualized, which enhanced model explainability and revealed that low-pass filtered wavelet coefficients, particularly the L3 band, were most informative for classification. Conclusion: Our handcrafted computationally lightweight model yielded accurate and explainable results. These attributes facilitate potential integration into digital stethoscopes for point-of-care screening of respiratory diseases.