College of Information Engineering, China Jiliang University, Hangzhou, China.
The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Sci Rep. 2023 Dec 12;13(1):22065. doi: 10.1038/s41598-023-49334-4.
Traditionally, the clinical evaluation of respiratory diseases was pulmonary function testing, which can be used for the detection of severity and prognosis through pulmonary function parameters. However, this method is limited by the complex process, which is impossible for patients to monitor daily. In order to evaluate pulmonary function parameters conveniently with less time and location restrictions, cough sound is the substitute parameter. In this paper, 371 cough sounds segments from 150 individuals were separated into 309 and 62 as the training and test samples. Short-time Fourier transform (STFT) was applied to transform cough sound into spectrogram, and ResNet50 model was used to extract 2048-dimensional features. Through support vector regression (SVR) model with biological attributes, the data were regressed with pulmonary function parameters, FEV1, FEV1%, FEV1/FVC, FVC, FVC%, and the performance of this models was evaluated with fivefold cross-validation. Combines with deep learning and machine learning technologies, the better results in the case of small samples were achieved. Using the coefficient of determination (R), the ResNet50 + SVR model shows best performance in five basic pulmonary function parameters evaluation as FEV1(0.94), FEV1%(0.84), FEV1/FVC(0.68), FVC(0.92), and FVC%(0.72). This ResNet50 + SVR hybrid model shows excellent evaluation of pulmonary function parameters during coughing, making it possible to realize a simple and rapid evaluation for pneumonia patients. The technology implemented in this paper is beneficial in judge the patient's condition, realize early screening of respiratory diseases, evaluate postoperative disease changes and detect respiratory infectious diseases without time and location restrictions.
传统上，呼吸系统疾病的临床评估是肺功能测试，它可以通过肺功能参数来检测严重程度和预后。然而，这种方法受到复杂过程的限制，患者不可能每天进行监测。为了方便地评估肺功能参数，且不受时间和地点的限制，咳嗽声成为了替代参数。在本文中，从 150 个人中分离出 371 段咳嗽声片段，分为 309 段和 62 段作为训练和测试样本。短时傅里叶变换（STFT）被应用于将咳嗽声转换为声谱图，然后使用 ResNet50 模型提取 2048 维特征。通过支持向量回归（SVR）模型与生物属性，将数据与肺功能参数 FEV1、FEV1%、FEV1/FVC、FVC、FVC%进行回归，使用五折交叉验证评估模型的性能。结合深度学习和机器学习技术，在小样本的情况下取得了更好的结果。使用决定系数（R），ResNet50+SVR 模型在五个基本肺功能参数的评估中表现出最好的性能，包括 FEV1（0.94）、FEV1%（0.84）、FEV1/FVC（0.68）、FVC（0.92）和 FVC%（0.72）。该 ResNet50+SVR 混合模型在咳嗽时对肺功能参数的评估表现出色，使得对肺炎患者进行简单快速的评估成为可能。本文实现的技术有利于判断患者病情，实现呼吸疾病的早期筛查，评估术后疾病变化，以及在不受时间和地点限制的情况下检测呼吸道传染病。
