Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases
Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases
复制标题
DOI：
10.1007/s10439-013-0741-6
复制
发表时间：
2013-05-01
影响因子：
3.8
通讯作者：
Triasih, Rina
中科院分区：
文献类型：
--
作者：
Swarnkar, Vinayak;Abeyratne, Udantha R.;Triasih, Rina
咳嗽是几种呼吸道疾病最常见的症状。它是身体的一种防御机制，以清除呼吸道意外吸入或由感染内部产生的异物。鉴别湿咳和干咳是一项重要的临床发现，有助于鉴别诊断，特别是在儿童中。湿咳更可能与下呼吸道细菌感染有关。目前，在典型的咨询会话期间，湿/干决定是基于医生的主观判断。它不适用于未经培训的人员、长期监测或治疗效果评估。在本文中，我们解决了这些问题，并开发了一种自动化技术，将咳嗽分为“湿”和“干”两类。我们提出了新的功能和Logistic回归模型（LRM）的分类咳嗽湿/干类。该方法的性能进行了评价的临床数据库中的儿科咳嗽（C = 536）使用床边非接触式麦克风从N = 78例患者记录。将自动分类的结果与两个专家人类评分员进行比较。LRM在挑选湿咳嗽方面的灵敏度和特异性在训练/验证数据集（来自60名患者的310个咳嗽事件）上为87%和88%之间，置信区间为95%，在前瞻性数据集（来自18名患者的117个咳嗽事件）上分别为84%和76%。在前瞻性数据集上，与两名专家人类评分员的Kappa一致率为0.51。这些结果表明该方法作为咳嗽监测的有用临床工具的潜力，特别是在家庭环境中。
Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children. Wet coughs are more likely to be associated with lower respiratory track bacterial infections. At present during a typical consultation session, the wet/dry decision is based on the subjective judgment of a physician. It is not available for the non-trained person, long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop an automated technology to classify cough into 'wet' and 'dry' categories. We propose novel features and a Logistic regression model (LRM) for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric coughs (C = 536) recorded using a bed-side non-contact microphone from N = 78 patients. Results of the automatic classification were compared against two expert human scorers. The sensitivity and specificity of the LRM in picking wet coughs were between 87 and 88% with 95% confidence interval on training/validation dataset (310 cough events from 60 patients) and 84 and 76% respectively on prospective dataset (117 cough events from 18 patients). The kappa agreement with two expert human scorers on prospective dataset was 0.51. These results indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.