A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease

TL;DR

This study introduces an XAI framework that analyzes cough spectrograms to identify spectral features associated with chronic respiratory diseases, improving interpretability and diagnostic accuracy.

Contribution

The paper develops a novel XAI-based spectral analysis method that decomposes cough spectrograms into frequency subbands for enhanced disease characterization.

Findings

Spectral patterns differ across subbands and disease groups.

The approach distinguishes COPD from other respiratory conditions.

It differentiates chronic from non-chronic patient groups.

Abstract

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.