A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease
This work addresses the need for interpretable diagnostic tools in respiratory medicine, offering a domain-specific method for analyzing cough acoustics, though it is incremental as it builds on existing XAI and CNN techniques.
The paper tackled the problem of analyzing cough sounds for chronic respiratory diseases like COPD by developing an XAI-based framework that uses occlusion maps on CNN-processed spectrograms to identify diagnostically relevant frequency subbands, resulting in the ability to distinguish COPD from other conditions and chronic from non-chronic groups based on interpretable spectral markers.
This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases, with a particular focus on Chronic Obstructive Pulmonary Disease (COPD). A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals, and occlusion maps are used to identify diagnostically relevant regions within the spectrograms. These highlighted areas are subsequently decomposed into five frequency subbands, enabling targeted spectral feature extraction and analysis. The results reveal that spectral patterns differ across subbands and disease groups, uncovering complementary and compensatory trends across the frequency spectrum. Noteworthy, the approach distinguishes COPD from other respiratory conditions, and chronic from non-chronic patient groups, based on interpretable spectral markers. These findings provide insight into the underlying pathophysiological characteristics of cough acoustics and demonstrate the value of frequency-resolved, XAI-enhanced analysis for biomedical signal interpretation and translational respiratory disease diagnostics.