基于深度学习的慢性阻塞性肺病的诊断模型研究

doi:10.3969/j.issn.0258-8021.2022.05.005

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Abstract Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease characterized by continuous airflow restriction, with high morbidity and mortality. At present, clinical diagnosis methods of COPD are very complex, not only time-consuming and invasive or radioactive, and not suitable for daily screening. Therefore, a COPD diagnosis model based on deep learning was designed in this study. Firstly, the lung sound from 42 COPD patients from RespiratoryDatabase@TR multimedia respiratory database were combined with the clinically collected lung sound from 24 COPD patients and 37 healthy subjects from Chest Hospital of Tianjin University, high-pass filter and denoising algorithm based on ensemble empirical mode decomposition (EEMD) and wavelet entropy was used for denoising. Secondly, the pre-processing process was completed through normalization, overlapping shear and data amplification. Thirdly, bispectrum analysis was used to extract the lung sound features. Finally, these features were input into an improved 19-layer convolutional neural network model to achieve the binary classification of healthy subjects and COPD patients. Experimental results showed that the proposed model could effectively diagnose COPD. The accuracy, sensitivity, specificity, F1 score, and Kappa score reached 98.93%, 98.47%, 99.41%, 98.95%, and 97.86%, respectively. Moreover, due to the use of bicentric data and denoising process, the model has higher reliability and is of important clinical significance.

Key words： chronic obstructive pulmonary disease lung sound bispectrum analysis convolutional neural network

Received: 14 September 2021

PACS:

R318

Corresponding Authors: * E-mail: sunjinglai@tju.edu.cn; zxydoctor@163.com

About author:: ^#Member, Chinese Society of Biomedical Engineering

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.05.005 OR http://cjbme.csbme.org/EN/Y2022/V41/I5/558

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