一种用于COPD自动检测的稳健深度学习方法
A Robust Deep Learning Approach for COPD Automated Detection.
摘要(中文译文)
慢性阻塞性肺疾病（COPD）仍然是一种普遍且使人衰弱的呼吸系统疾病，需要早期准确的诊断以实现最佳的临床干预。在本研究中，我们提出了一种基于深度学习的新型诊断框架，采用ECAPA-TDNN（强调通道注意力、传播与聚合-时延神经网络）架构对ICBHI数据集中的呼吸音信号进行分类。ECAPA-TDNN最初为说话人验证而设计，引入了通道注意力和多尺度特征聚合，我们首次将其适配到医学声学分析领域。该架构使模型能够有效捕捉病理性呼吸音中细微且具有判别性的模式，克服了传统基于CNN方法的局限性。我们的方法整合了严格的信号预处理、对数梅尔频谱图提取和数据增强，以增强鲁棒性和泛化能力。采用注意力统计池化机制进行时间特征汇总，同时引入基于Grad-CAM的可解释性方法以提高诊断预测的可解释性。该模型通过五折交叉验证方案进行严格验证，平均验证准确率达到96.8%，且在所有折中均保持较高的F1分数和召回率。与先前方法的对比分析凸显了我们基于ECAPA-TDNN的方法在诊断精度、鲁棒性和潜在临床适用性方面的优越性。据我们所知，这是首个将ECAPA-TDNN适配用于呼吸音COPD检测的工作，为可解释且高性能的基于声学的呼吸系统疾病筛查建立了新的基准。
Abstract
COPD remains a prevalent and debilitating respiratory condition, necessitating early and accurate diagnosis for optimal clinical intervention. In this study, we propose a novel deep learning-based diagnostic framework that employs the ECAPA-TDNN (Emphasized Channel Attention, Propagation and Aggregation-Time Delay Neural Network) architecture to classify respiratory sound signals from the ICBHI dataset. Originally designed for speaker verification, ECAPA-TDNN introduces channel attention and multi-scale feature aggregation, which we adapt for the first time to the domain of medical acoustic analysis. This architecture allows the model to effectively capture subtle and discriminative patterns in pathological breathing sounds, overcoming the limitations of conventional CNN-based methods. Our methodology integrates rigorous signal preprocessing, log-Mel spectrogram extraction, and data augmentation to enhance robustness and generalization. An Attentive Statistics Pooling mechanism is employed for temporal feature summarization, while Grad-CAM-based explainability is incorporated to improve the interpretability of the diagnostic predictions. The model is rigorously validated using a five-fold cross-validation scheme, achieving a mean validation accuracy of 96.8% with consistently high F1-scores and recall rates across all folds. Comparative analysis with prior methods highlights the superiority of our ECAPA-TDNN-based approach in terms of diagnostic precision, robustness, and potential clinical applicability. To the best of our knowledge, this is the first work to adapt ECAPA-TDNN for COPD detection from respiratory sounds, establishing a new benchmark in interpretable and high-performance acoustic-based respiratory disease screening.
如何引用
AMA
Shuting Xu, Ravinesh C Deo, Salvin S Prasad, Prabal D Barua, Jeffrey Soar, Rajendra Acharya. A Robust Deep Learning Approach for COPD Automated Detection.. Sensors (Basel, Switzerland). 2026; doi:10.3390/s26092713.
APA
Shuting Xu, Ravinesh C Deo, Salvin S Prasad, Prabal D Barua, Jeffrey Soar, Rajendra Acharya (2026). A Robust Deep Learning Approach for COPD Automated Detection.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26092713
GB/T 7714
Shuting Xu, Ravinesh C Deo, Salvin S Prasad, Prabal D Barua, Jeffrey Soar, Rajendra Acharya. A Robust Deep Learning Approach for COPD Automated Detection.[J]. Sensors (Basel, Switzerland), 2026 doi:10.3390/s26092713.
在 PubMed增强版 阅读全文
翻译全文 · AI 文献问答 · PDF 原文下载 · 引用导出