Wang Jiawen, Min Chunyan, Yu Feng, Chen Kai, Mao Ling
School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, China.
Department of Pneumoconiosis, Shanghai Pulmonary Hospital, Tongji University, Shanghai, China.
BMC Med Inform Decis Mak. 2025 Jan 27;25(1):41. doi: 10.1186/s12911-025-02879-y.
Many respiratory diseases such as pneumoconiosis require to close monitor the symptoms such as abnormal respiration and cough. This study introduces an automated, nonintrusive method for detecting cough events in clinical settings using a flexible chest patch with tri-axial acceleration sensors.
Twenty-five young healthy persons (hereinafter referred to as healthy adults) and twenty-five clinically diagnosed pneumoconiosis patients (hereinafter referred to as patients) participated in the experiment by wearing a flexible chest patch with an embedded ACC sensor. The top 56% of the highest scoring features were then combined using several feature selection algorithms to perform the cough classification task. The multicriteria decision making (MCDM) method was used to select the classifier with the highest scores.
The optimized classifier proposed in this paper achieved an accuracy of 87.1%, precision of 95%, recall of 79.1%, F1 score of 86.4%, and AUC of 95.4% for recognizing coughs in healthy adults; an accuracy of 96.1%, precision of 95%, recall of 97.4%, F1 score of 96.2%, and AUC of 98.7% for recognizing coughs in patients; and an overall accuracy of 92% for distinguishing coughs in the combined group of healthy adults and patients.
Our study demonstrated the effectiveness of an automated cough recognition system in both pneumoconiosis patients and healthy adults. This approach facilitates daily remote monitoring of cough occurrence in individuals with pneumoconiosis, potentially enhancing the ability of physicians to evaluate clinical status.
许多呼吸系统疾病，如尘肺病，需要密切监测呼吸异常和咳嗽等症状。本研究介绍了一种使用带有三轴加速度传感器的柔性胸部贴片在临床环境中检测咳嗽事件的自动化、非侵入性方法。
25名年轻健康人（以下简称健康成年人）和25名临床诊断为尘肺病的患者（以下简称患者）通过佩戴嵌入ACC传感器的柔性胸部贴片参与实验。然后使用几种特征选择算法组合得分最高的前56%的特征来执行咳嗽分类任务。采用多准则决策（MCDM）方法选择得分最高的分类器。
本文提出的优化分类器在识别健康成年人咳嗽时，准确率为87.1%，精确率为95%，召回率为79.1%，F1分数为86.4%，AUC为95.4%；在识别患者咳嗽时，准确率为96.1%，精确率为95%，召回率为97.4%，F1分数为96.2%，AUC为98.7%；在区分健康成年人与患者的联合组咳嗽时，总体准确率为92%。
我们的研究证明了自动化咳嗽识别系统在尘肺病患者和健康成年人中的有效性。这种方法有助于对尘肺病患者的咳嗽发生情况进行日常远程监测，可能增强医生评估临床状况的能力。
