具有空间-通道重构卷积模块的肺音分类模型  

A lung sound classification model with a spatial and channel reconstruction convolutional module

作　　者：叶娜 吴辰文 蒋佳霖 YE Na;WU Chenwen;JIANG Jialin(Department of Computer Science and Technology,College of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院计算机科学与技术系,甘肃兰州730070

出　　处：《南方医科大学学报》2024年第9期1720-1728,共9页Journal of Southern Medical University

基　　金：国家自然科学基金(62241204)。

摘　　要：目的探究肺音数据的准确识别及分类。方法本文提出了一种结合空间-通道重构卷积(SCConv)模块的卷积网络架构以及双可调Q因子小波变换(DTQWT)与三重Wigner-Ville变换(WVT)结合的肺音特征提取方法,通过自适应地聚焦于重要的通道和空间特征,提高模型对肺音关键特征的捕捉能力。基于ICBHI2017数据集,进行正常音、哮鸣音、爆裂音、哮鸣音和爆裂音结合的分类。结果方法在分类的准确率、敏感性、特异性以及F1分数上分别达到85.68%、93.55%、86.79%、90.51%。结论所提方法在ICBHI 2017肺音数据库上取得了优异的性能,特别是在区分正常肺音和异常肺音方面。Objective To construct a model with a spatial and channel reconstruction convolutional module for accurate identification and classification of lung sound data.Method We propose a convolutional network architecture combining the spatial-channel reconstruction convolution(SCConv)module.A lung sound feature extraction method combining the dual tunable Q-factor wavelet transform(DTQWT)with the triple Wigner-Ville transform(WVT)was used to improve the model's ability to capture the key features of the lung sounds by adaptively focusing on the important channel and spatial features.The performance of the model for classification of normal,crackles,wheezes,and crackles with wheezes was tested using the ICBHI2017 dataset.Results and Conclusion The accuracy,sensitivity,specificity and F1 score of the proposed method reached 85.68%,93.55%,86.79%and 90.51%,respectively,demonstrating its good performance in classification tasks in the ICBHI2017 lung sound database,especially for distinguishing normal from abnormal lung sounds.

关 键 词：肺音分类 卷积神经网络 空间-通道重构卷积 双可调Q因子小波变换 三重Wigner-Ville变换 

分 类 号：TN912.3[电子电信—通信与信息系统] R563.9[电子电信—信息与通信工程] TP18[医药卫生—呼吸系统]