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.

翻译：本文提出一种基于可解释人工智能（XAI）的方法论，以增强对呼吸系统疾病管理中咳嗽声分析的理解。我们采用遮挡图来凸显经卷积神经网络（CNN）处理的咳嗽声谱图中的相关频谱区域。随后，对这些经遮挡图加权的声谱图进行频谱分析，揭示了不同疾病组间的显著差异，尤其在慢性阻塞性肺疾病（COPD）患者中，咳嗽模式在识别出的感兴趣频谱区域内表现出更高的变异性。这与分析原始声谱图时未观察到显著差异的情况形成对比。所提出的方法提取并分析了多项频谱特征，证明了XAI技术在揭示疾病特异性声学特征方面的潜力，并通过提供更具可解释性的结果，提升了咳嗽声分析的诊断能力。