Detection of COVID-19 by Cough Sound: A Method Based on DSC + BiLSTM

In recent years, the outbreak of COVID-19 has brought a new round of challenges to global health care, and daily large-scale testing has also increased the consumption of medical resources. However, studies have shown that the cough sounds of patients with COVID-19 are significantly different from o...

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Bibliographic Details
Published in2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC) pp. 1 - 5
Main Authors Meng, Jie, Zhang, Peng, Wang, Jianhua, Wang, Aohui, Zhang, Long
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.12.2022
Subjects
CNN+BILSTM
cough
COVID-19
Infectious diseases
Medical services
Splicing
Time series analysis
Training
Wireless communication
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Summary:In recent years, the outbreak of COVID-19 has brought a new round of challenges to global health care, and daily large-scale testing has also increased the consumption of medical resources. However, studies have shown that the cough sounds of patients with COVID-19 are significantly different from other Characteristics of respiratory infectious diseases. Therefore, this paper considers the use of the patient's cough as a detection sample to give the preliminary screening results. The research was conducted on the COUGHVID dataset. The experiment is divided into two stages: (1) Preprocessing stage: use Pitch Shift and Time Stretch to perform data enhancement on audio data, and use spec Augment to perform data enhancement on mel spectrogram. (2) Model construction stage: use two layers of DSC and one layer of BILSTM to splicing to obtain a classification model. Finally, the method is compared with the baseline method using only two layers of LSTM. The results show that accuracy has increased by 1.9%, F1 has increased by 1.9%, and AUC has increased by 1.6%.
DOI:10.1109/ICMNWC56175.2022.10031770