School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Department of Pediatric Cardiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
PLoS One. 2022 Oct 27;17(10):e0275479. doi: 10.1371/journal.pone.0275479. eCollection 2022.
Bronchitis and pneumonia are the common respiratory diseases, of which pneumonia is the leading cause of mortality in pediatric patients worldwide and impose intense pressure on health care systems. This study aims to classify bronchitis and pneumonia in children by analyzing cough sounds. We propose a Classification Framework based on Cough Sounds (CFCS) to identify bronchitis and pneumonia in children. Our dataset includes cough sounds from 173 outpatients at the West China Second University Hospital, Sichuan University, Chengdu, China. We adopt aggregation operation to obtain patients' disease features because some cough chunks carry the disease information while others do not. In the stage of classification in our framework, we adopt Support Vector Machine (SVM) to classify the diseases due to the small scale of our dataset. Furthermore, we apply data augmentation to our dataset to enlarge the number of samples and then adopt Long Short-Term Memory Network (LSTM) to classify. After 45 random tests on RAW dataset, SVM achieves the best classification accuracy of 86.04% and standard deviation of 4.7%. The precision of bronchitis and pneumonia is 93.75% and 87.5%, and their recall is 88.24% and 93.33%. The AUC of SVM and LSTM classification models on the dataset with pitch-shifting data augmentation reach 0.92 and 0.93, respectively. Extensive experimental results show that CFCS can effectively classify children into bronchitis and pneumonia.
支气管炎和肺炎是常见的呼吸道疾病，其中肺炎是全球儿科患者死亡的主要原因，并给医疗保健系统带来巨大压力。本研究旨在通过分析咳嗽声对儿童支气管炎和肺炎进行分类。我们提出了一种基于咳嗽声的分类框架（CFCS）来识别儿童的支气管炎和肺炎。我们的数据集包括来自中国四川大学华西第二医院 173 名门诊患者的咳嗽声。我们采用聚合操作来获取患者的疾病特征，因为有些咳嗽片段携带疾病信息，而有些则不携带。在我们框架的分类阶段，由于数据集规模较小，我们采用支持向量机（SVM）对疾病进行分类。此外，我们对数据集进行了数据增强，以增加样本数量，然后采用长短期记忆网络（LSTM）进行分类。在对 RAW 数据集进行 45 次随机测试后，SVM 实现了最佳的分类准确率 86.04%和标准差 4.7%。支气管炎和肺炎的准确率分别为 93.75%和 87.5%，召回率分别为 88.24%和 93.33%。在具有音高移位数据增强的数据集上，SVM 和 LSTM 分类模型的 AUC 分别达到 0.92 和 0.93。大量实验结果表明，CFCS 可以有效地对儿童进行支气管炎和肺炎分类。
