基于CNN的呼吸声音分类,使用频谱描述符

S. Jayalakshmy, B. Priya, N. Kavya
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引用次数: 4

摘要

慢性阻塞性肺疾病(COPD)是一种可怕的疾病,包括肺气肿、支气管炎等。它威胁着全世界近300万人的生命。在卷积神经网络(CNN)等深度学习模型的帮助下,基于肺音分析可以更好地检测COPD的诊断。本文采用多类分类器对正常呼吸音和喘鸣、噼啪、隆奇等异常呼吸音的存在进行分类。从线性谱中提取光谱描述子特征,从Mel谱中提取MFCC特征。为了实验和分类,本工作共考虑了596个肺声信号。与二元机器学习分类器相比,使用K-NN和决策树等分类器获得了更高的准确率。结果表明,采用深度学习CNN模型的多类分类器总体准确率达到96.7%。并将多类分类器的分类结果与SVM分类器进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN based Categorization of respiratory sounds using spectral descriptors
Chronic Obstructive Pulmonary Disease (COPD) is a dreadful disease which is a wide umbrella comprises of emphysema, bronchitis etc. It threatens the life of almost nearly 3 million people all over the world. The diagnosis of COPD can be detected in a better manner based on the lung sound analysis with the help of deep learning models such as convolutional neural network (CNN). In this work, the presence of COPD with different class of the sound like normal breathe sounds and abnormal breathe sounds such as wheeze, crackle and rhonchi are classified by using multi-class classifier. Spectral descriptor features from linear spectrum and MFCC from Mel spectrum are extracted. For experimentation and classification, a total of 596 lung sound signals are considered in this work. The classifier such as K-NN and decision tree are used to obtain an improved accuracy compared to binary machine learning classifier. The results indicates than an overall accuracy of 96.7% is obtained with multi-class classifiers using deep learning CNN model. The multi-class classifier results are also compared with SVM classifier.
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