Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses
This work addresses the challenge of individual differences in biomedical signals for early diagnosis of respiratory diseases, offering an incremental improvement in patient-centered healthcare.
The paper tackled the problem of inter-patient variability in lung sound classification by proposing a Patient-Aware Feature Alignment framework with novel losses, achieving scores of 64.84% for four-class and 72.08% for two-class classification on the ICBHI dataset.
Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability even among patients with the same symptoms, requiring a learning approach that considers individual differences. We propose a Patient-Aware Feature Alignment (PAFA) framework with two novel losses, Patient Cohesion-Separation Loss (PCSL) and Global Patient Alignment Loss (GPAL). PCSL clusters features of the same patient while separating those from other patients to capture patient variability, whereas GPAL draws each patient's centroid toward a global center, preventing feature space fragmentation. Our method achieves outstanding results on the ICBHI dataset with a score of 64.84\% for four-class and 72.08\% for two-class classification. These findings highlight PAFA's ability to capture individualized patterns and demonstrate performance gains in distinct patient clusters, offering broader applications for patient-centered healthcare.