用可解释AI分析咳嗽声谱，发现肺病患者特有的声音特征。
XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization
- 通过遮挡图定位咳嗽频谱中关键区域，提升分析精度。
- 慢性阻塞性肺病患者在特定频段的咳嗽模式差异显著。
- 结果更可解释，适合临床辅助诊断与医学研究者使用。
本文提出一种可解释人工智能（XAI）驱动的方法，以增强对咳嗽声谱分析的理解，用于呼吸系统疾病管理。我们采用遮挡图（occlusion maps）识别卷积神经网络（CNN）处理咳嗽声谱图时的关键频段区域。随后，对这些区域加权后的声谱进行分析，揭示了不同疾病群体间的显著差异，尤其在慢性阻塞性肺病（COPD）患者中，关键频段的咳嗽模式表现出更高变异性。相比之下，直接分析原始声谱图未发现显著差异。该方法提取并分析多个频谱特征，表明XAI技术能够挖掘出具有疾病特异性的声学标志，提升咳嗽声分析的诊断能力，并提供更可解释的结果。
原文摘要 · Abstract (English)
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.
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