XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization
This work addresses the need for more interpretable diagnostic tools in respiratory disease management, though it is incremental as it applies existing XAI techniques to a specific domain.
The paper tackled the problem of interpreting cough sound analysis for respiratory disease diagnosis by using an XAI-driven method to highlight relevant spectral regions in cough spectrograms, resulting in the identification of significant differences between disease groups, such as more variable cough patterns in COPD patients, which were not detectable with raw spectrograms.
This paper proposes an eXplainable Artificial Intelligence (XAI)-driven methodology to enhance the understanding of cough sound analysis for respiratory disease management. We employ occlusion maps to highlight relevant spectral regions in cough spectrograms processed by a Convolutional Neural Network (CNN). Subsequently, spectral analysis of spectrograms weighted by these occlusion maps reveals significant differences between disease groups, particularly in patients with COPD, where cough patterns appear more variable in the identified spectral regions of interest. This contrasts with the lack of significant differences observed when analyzing raw spectrograms. The proposed approach extracts and analyzes several spectral features, demonstrating the potential of XAI techniques to uncover disease-specific acoustic signatures and improve the diagnostic capabilities of cough sound analysis by providing more interpretable results.