PDF(Pubmed)
Abstract：
Chronic Obstructive Pulmonary Disease (COPD), as the third leading cause of death worldwide, is a major global health issue. The early detection and grading of COPD are pivotal for effective treatment. Traditional spirometry tests, requiring considerable physical effort and strict adherence to quality standards, pose challenges in COPD diagnosis. Volumetric capnography (VCap), which can be performed during natural breathing without requiring additional compliance, presents a promising alternative tool. In this study, the dataset comprised 279 subjects with normal pulmonary function and 148 patients diagnosed with COPD. We introduced a novel quantitative analysis method for VCap. Volumetric capnograms were converted into two-dimensional grayscale images through the application of Gramian Angular Field (GAF) transformation. Subsequently, a multi-scale convolutional neural network, CapnoNet, was conducted to extract features and facilitate classification. To improve CapnoNet\'s performance, two data augmentation techniques were implemented. The proposed model exhibited a detection accuracy for COPD of 95.83%, with precision, recall, and F1 measures of 95.21%, 95.70%, and 95.45%, respectively. In the task of grading the severity of COPD, the model attained an accuracy of 96.36%, complemented by precision, recall, and F1 scores of 88.49%, 89.99%, and 89.15%, respectively. This work provides a new perspective for the quantitative analysis of volumetric capnography and demonstrates the strong performance of the proposed CapnoNet in the diagnosis and grading of COPD. It offers direction and an effective solution for the clinical application of capnography.
摘要：
慢性阻塞性肺疾病(COPD),作为全球第三大死亡原因，是一个重大的全球健康问题。COPD的早期发现和分级是有效治疗的关键。传统的肺活量测定测试，需要相当大的体力和严格遵守质量标准，对COPD诊断构成挑战。容量二氧化碳描记术(VCap),这可以在自然呼吸期间进行，而不需要额外的依从性，提出了一个有前途的替代工具。在这项研究中,该数据集包括279例肺功能正常的受试者和148例诊断为COPD的患者.我们介绍了一种新的VCap定量分析方法。通过应用Gramian角场（GAF）变换将体积二氧化碳图转换为二维灰度图像。随后，多尺度卷积神经网络，CapnoNet,进行特征提取和便于分类。为了提高CapnoNet的性能，实施了两种数据增强技术.提出的模型对COPD的检测准确率为95.83%，精确地，召回，F1措施为95.21%，95.70%,和95.45%，分别。在对COPD严重程度进行分级的任务中，该模型达到了96.36%的准确率，辅以精度，召回，F1得分为88.49%，89.99%,和89.15%，分别。这项工作为体积二氧化碳图的定量分析提供了新的视角，并证明了拟议的CapnoNet在COPD的诊断和分级中的强大性能。为二氧化碳监测的临床应用提供了方向和有效的解决方案。
