文献类型：期刊文献

英文题名：Research on lung sound classification model based on dual-channel CNN-LSTM algorithm

作者：Zhang, Yipeng[1,2];Huang, Qiong[1];Sun, Wenhui[1,2];Chen, Fenlan[3];Lin, Dongmei[4];Chen, Fuming[1]

第一作者：Zhang, Yipeng;张艳萍;张郁萍;张玉萍

通信作者：Chen, FM[1]

机构：[1]Chinese Peoples Liberat Army, Med Secur Ctr, Hosp Joint Logist Support Force 940, Lanzhou 730050, Gansu, Peoples R China;[2]Gansu Univ Tradit Chinese Med, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China;[3]Lanzhou Rail Transit Co Ltd, Lanzhou 730030, Gansu, Peoples R China;[4]Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China

第一机构：Chinese Peoples Liberat Army, Med Secur Ctr, Hosp Joint Logist Support Force 940, Lanzhou 730050, Gansu, Peoples R China

通信机构：[1]corresponding author), Chinese Peoples Liberat Army, Med Secur Ctr, Hosp Joint Logist Support Force 940, Lanzhou 730050, Gansu, Peoples R China.

年份：2024

卷号：94

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20241315828974);Scopus(收录号：2-s2.0-85188801991);WOS:【SCI-EXPANDED(收录号:WOS:001223059900001)】；

基金：Funding This research was funded by Natural Science Foundation of China (61901515, 62361038) , Natural Science Foundation of Gansu Province (22JR5RA002) and the Innovation Fund for University Teachers of Department of Education of Gansu Province (2023A-019) .

语种：英文

外文关键词：Lung sound classification; Mel cepstral coefficient; Convolutional neural network; Long short-term memory network

摘要：ulmonary diseases have a significant impact on human health and life safety, and abnormalities in the lungs are a direct response to lung diseases. Establishing an effective lung sound classification model that can assist in diagnosis is of great significance for electronic auscultation.In addressing the issue of lung sound signal classification, this study introduces a deep learning classification model based on a dual-channel CNN-LSTM algorithm. Initially, Mel-scale Frequency Cepstral Coefficients (MFCC) are employed for feature extraction from the dataset, transforming lung sound signals into Mel spectrograms. On this foundation, a dual-channel algorithm classification model is constructed, with parallel Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) modules. The CNN module is designed to capture spatial dimension features of the input data, while the LSTM module focuses on temporal dimension features. These two feature sets are fused together, enabling the model to classify lung sounds and thereby assisting in diagnosing pulmonary diseases for healthcare practitioners. This experiment used the ICBHI2017 Challenge Lungs dataset and obtained 5054 pieces of data through data augmentation and sampling techniques.The results show that the accuracy, recall, and F1 score of this model reach 99.01%, 99.13%, and 0.9915, respectively, significantly superior to other models, highlighting its practical application value.