XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization

TL;DR

This study introduces an XAI-based spectral analysis method for cough sounds, revealing disease-specific acoustic signatures and improving interpretability in respiratory disease diagnosis.

Contribution

The paper presents a novel XAI-driven approach using occlusion maps to identify spectral regions relevant for disease differentiation in cough sounds.

Findings

Spectral regions identified by XAI differ significantly between disease groups.

Variable cough patterns are observed in COPD patients in key spectral regions.

XAI techniques enhance interpretability and diagnostic potential of cough sound analysis.

Abstract

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.