用可解释AI分析咳嗽声谱，区分慢阻肺等呼吸疾病
A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease
- 通过卷积神经网络与遮挡图定位关键频段
- 不同疾病在五个频带呈现差异性光谱特征
- 结果可解释，适合临床辅助诊断研究者
本文提出一种基于可解释人工智能（XAI）的框架，用于慢性呼吸系统疾病（尤其是慢性阻塞性肺病，COPD）相关咳嗽声音的频谱分析。采用卷积神经网络（CNN）对咳嗽信号的时间-频率表示进行训练，并利用遮挡图（occlusion maps）识别光谱图中具有诊断意义的区域。这些被突出的区域随后被分解为五个频率子带，实现针对性的频谱特征提取与分析。结果显示，不同子带和疾病组之间的光谱模式存在显著差异，揭示了频谱范围内的互补与代偿趋势。该方法能够基于可解释的频谱标记区分COPD与其他呼吸疾病，以及慢性与非慢性患者群体。研究为咳嗽声学背后的病理生理机制提供了新见解，并展示了频率解析、结合XAI的生物医学信号分析在转化性呼吸疾病诊断中的价值。
原文摘要 · Abstract (English)
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.
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