哮喘联盟

   摘要
   咳嗽是多种呼吸道疾病的常见症状。它是机体清除呼吸道意外吸入或感染后内部产生的异物的一种防御性机制。湿咳和干咳的诊断，对于儿童咳嗽的鉴别诊断至关重要。湿咳更有可能与下呼吸道细菌性感染有关。目前，在临床就诊过程中，湿/干咳的诊断主要基于医生的主观判断。对于未接受培训的医务人员，尚无法进行长期监测或评价治疗疗效。在本文中，我们对上述问题进行了阐述，并开发了一种自动技术来鉴别干咳和湿咳。我们提出的新型特征和logistic回归模型（LRM），可将咳嗽分为湿/干咳。采用床旁非接触式话筒，基于78名儿童咳嗽患者的临床数据库（C = 536），对该模型进行评价。将自动分类结果与两名专家的评判结果进行比较。对于培训/验证数据集，LRM判断湿咳的敏感性和特异性分别为87%和88%（60名患者的310次咳嗽）；对于前瞻性数据集，分别为84%和76%（18名患者的117次咳嗽）。对于前瞻性数据集，与两名专家评判结果之间的kappa一致性为0.51。这些结果显示，该方法可作检测咳嗽的临床工具，特别适合于家庭使用。

（林江涛 审校）
Ann Biomed Eng. 2013 Jan 25. [Epub ahead of print]

 

Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases.
 
Swarnkar V, Abeyratne UR, Chang AB, Amrulloh YA, Setyati A, Triasih R.

Source
School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia Campus, Brisbane, Australia.

Abstract 
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.
 

Ann Biomed Eng. 2013 Jan 25. [Epub ahead of print]