基于傅立叶分解的健康、慢性阻塞性肺病和哮喘的单通道肺音自动分类法

IF 3.4 Q2 ENGINEERING, BIOMEDICAL IEEE transactions on medical robotics and bionics Pub Date : 2024-06-03 DOI:10.1109/TMRB.2024.3408325
Vaibhav Koshta;Bikesh Kumar Singh;Ajoy K. Behera;Ranganath T. G.
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引用次数: 0

摘要

对哮喘和慢性阻塞性肺病(COPD)进行主观判别具有挑战性,因为这两种疾病的症状有重叠之处,而且会受到个人解释的影响。因此,需要一种没有任何主观干扰的替代诊断系统。本研究介绍了基于傅立叶分解法(FDM)的模型,利用离散余弦变换(DCT)和离散傅立叶变换(DFT),通过分析肺部声音信号来识别哮喘和慢性阻塞性肺病患者。使用三个滤波器组将信号分解为傅里叶本征频带函数(FIBF),这三个滤波器组分别是:二元滤波器组、等能滤波器组和均一滤波器组。四个统计属性,即根据相关的 FIBF 计算出香农熵、对数熵、中位数绝对偏差和峰度。支持向量机(SVM)、k-近邻(kNN)和经贝叶斯优化的集合分类器(EC)分别用于哮喘与慢性阻塞性肺病和正常与临近声的分类。使用 DCT 和 10 倍交叉验证取得的最高准确率如下:99.4%(哮喘 vs 慢性阻塞性肺病)、99.1%(哮喘 vs 慢性阻塞性肺病 vs 正常)、99.4%(慢性阻塞性肺病 vs 正常)和 99.7%(哮喘 vs 正常)。同样,DFT 通过 10 倍交叉验证报告的最高准确率为99.4%(哮喘与慢性阻塞性肺病)、99.6%(哮喘与慢性阻塞性肺病与正常)、99.4%(慢性阻塞性肺病与正常)和 99.8%(哮喘与正常)。
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Fourier Decomposition-Based Automated Classification of Healthy, COPD, and Asthma Using Single-Channel Lung Sounds
Subjective discrimination of asthma and Chronic Obstructive Pulmonary Disease (COPD) is challenging as they share overlapping symptoms and are subject to personal interpretation. Hence, there is a demand for an alternative diagnostic system devoid of any subjective interference. The current study introduces Fourier Decomposition Method (FDM) based models utilizing Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) to identify patients with asthma and COPD by analyzing lung sound signals. The signals were decomposed into Fourier intrinsic band functions (FIBF) using three filter banks: dyadic, equal energy, and uniform band. Four statistical attributes, namely: Shannon entropy, log entropy, median absolute deviation and kurtosis, are calculated from relevant FIBF. Support vector machine (SVM), k-nearest neighbor (kNN) and ensemble classifier (EC) optimized with Bayesian optimization are used for classification of asthma vs COPD and normal vs adventitious sound, respectively. The highest accuracies achieved using DCT with 10-fold cross-validation are as follows: 99.4% (Asthma vs COPD), 99.1% (Asthma vs COPD vs Normal), 99.4% (COPD vs Normal) and 99.7% (Asthma vs Normal). Similarly, the highest accuracies reported by DFT with 10-fold cross-validation are: 99.4% (Asthma vs COPD), 99.6% (Asthma vs COPD vs Normal), 99.4% (COPD vs Normal) and 99.8% (Asthma vs Normal).
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Table of Contents IEEE Transactions on Medical Robotics and Bionics Society Information Guest Editorial Special section on the Hamlyn Symposium 2023—Immersive Tech: The Future of Medicine IEEE Transactions on Medical Robotics and Bionics Publication Information IEEE Transactions on Medical Robotics and Bionics Information for Authors
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