文献类型：期刊文献

中文题名：基于小波变换和CNN-LSTM的肺音分类算法

英文题名：Lung sound classification algorithm based on wavelet transform and CNN-LSTM

作者：张乙鹏[1,2];孙文慧[1,2];陈扶明[2]

第一作者：张乙鹏

机构：[1]甘肃中医药大学信息工程学院,甘肃兰州730000;[2]中国人民解放军联勤保障部队第940医院医疗保障中心,甘肃兰州730050

第一机构：甘肃中医药大学信息工程学院（教育技术中心）

年份：2024

卷号：41

期号：3

起止页码：356

中文期刊名：中国医学物理学杂志

外文期刊名：Chinese Journal of Medical Physics

收录：CSTPCD;;CSCD:【CSCD_E2023_2024】；

基金：国家自然科学基金(61901515,62361038);甘肃省自然科学基金(22JR5RA002)。

语种：中文

中文关键词：肺音分类;小波变换;卷积神经网络;长短期记忆网络

外文关键词：lung sound classification;wavelet transform;convolutional neural network;long short-term memory

摘要：目的:针对如何建立有助于电子听诊诊断的肺音分类模型,提出一种基于卷积神经网络(CNN)-长短期记忆网络(LSTM)的混合深度学习肺音分类模型方法。方法:首先使用小波变换对数据集进行特征提取,使肺音信号转化为能量熵、峰值等特征;在此基础上构建CNN和LSTM的混合算法分类模型,其中将小波变换提取的特征先输入CNN模块,能够获得数据的空间维度特征,再通过LSTM模块获得数据的时间维度特征,融合两类特征,通过模型可以将肺音分类,从而达到辅助判断患者的肺部疾病。结果:CNN-LSTM混合模型准确率、F1分数均明显高于其他单一模型,可达到0.948和0.950。结论:提出的CNN-LSTM混合模型分类准确率更高,在智能听诊领域具有广泛的潜在应用价值。
Objective To establish a hybrid deep learning lung sound classification model based on convolutional neural network(CNN)-long short-term memory(LSTM)for electronic auscultation.Methods Wavelet transform was used to extract features from the dataset,transforming lung sound signals into energy entropy,peak value and other features.On this basis,a classification model based on hybrid algorithm incorporating CNN and LSTM neural network was constructed.The features extracted by wavelet transform were input into CNN module to obtain the spatial features of the data,and then the temporal features were detected through LSTM module.The fusion of the two types of features enabled the classification of lung sounds through the model,thereby assisting in the diagnosis of pulmonary diseases.Results The accuracy rate and F1 score of CNN-LSTM hybrid model were significantly higher than those of other single models,reaching 0.948 and 0.950.Conclusion The proposed CNN-LSTM hybrid model demonstrates higher accuracy and more precise classification,showcasing broad potential application value in intelligent auscultation.