利用复杂网络提取慢性阻塞性肺病特征的高性能方法

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Trong-Thanh Han, Kien Le Trung, Phuong Nguyen Anh, Phat Nguyen Huu
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引用次数: 0

摘要

目的: 本文提出了一种利用呼吸声属性对慢性阻塞性肺病(COPD)进行分类的新方法。应用频谱变换和各种小波变换来捕捉不同的信号特征。此外,还采用了复杂网络来提取特征元素,根据图因素(包括熵、密度和位置)生成新的频谱图数据表示。然后,使用六种机器学习算法对归一化和丰富的数据开发 COPD 分类器,并通过适当的训练细节和超参数调整进行微调。值得注意的是,随机森林算法的 AUC 高达 99.67%,优于其他算法。此外,小波道别奇斯 2 算法(Db2)的准确率一直接近 98%,与 Naive Bayes 算法结合使用尤其值得注意。反变换、复杂网络和优化分类算法的应用产生了超出预期的结果。该方法利用应用于呼吸音分析的机器学习技术,为准确诊断慢性阻塞性肺病提供了一种可行的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High performance method for COPD features extraction using complex network.

Objectives. The paper proposes a novel methodology for the classification of Chronic Obstructive Pulmonary Disease (COPD) utilizing respiratory sound attributes.Methods. The approach involves segmenting respiratory sounds into individual breaths and conducting extensive studies on this dataset. Spectral Transforms, various Wavelet Transforms are applied to capture distinct signal features. Complex Network is also employed to extract characteristic elements, generating novel representations of spectrogram data based on graph factors, including entropy, density, and position. The normalized and enriched data is then used to develop COPD classifiers using six machine learning algorithms, fine-tuning with appropriate training details and hyperparameter tuning.Results. Our results demonstrate robust performance, with ROC curves consistently exhibiting an Area Under the Curve (AUC) > 96% across different time-frequency transformations. Notably, the Random Forest algorithm achieves an AUC of 99.67%, outperforming other algorithms. Moreover, the Wavelet Daubechies 2 (Db2) consistently approaches 98% accuracy, particularly noteworthy in conjunction with the Naive Bayes algorithm.Conclusion. This study diagnosis patients through spectrogram images extracted from lung sounds. The application of Inverse Transforms, Complex Network, and Optimized Classification Algorithms yielded results beyond expectations. This methodology provides a promising approach for accurate COPD diagnosis, leveraging Machine Learning techniques applied to respiratory sound analysis.

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来源期刊
Biomedical Physics & Engineering Express
Biomedical Physics & Engineering Express RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.80
自引率
0.00%
发文量
153
期刊介绍: BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.
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