School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia,
Ann Biomed Eng. 2013 Nov;41(11):2448-62. doi: 10.1007/s10439-013-0836-0. Epub 2013 Jun 7.
Pneumonia annually kills over 1,800,000 children throughout the world. The vast majority of these deaths occur in resource poor regions such as the sub-Saharan Africa and remote Asia. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths. The reliable diagnosis of childhood pneumonia in remote regions is fraught with difficulties arising from the lack of field-deployable imaging and laboratory facilities as well as the scarcity of trained community healthcare workers. In this paper, we present a pioneering class of technology addressing both of these problems. Our approach is centred on the automated analysis of cough and respiratory sounds, collected via microphones that do not require physical contact with subjects. Cough is a cardinal symptom of pneumonia but the current clinical routines used in remote settings do not make use of coughs beyond noting its existence as a screening-in criterion. We hypothesized that cough carries vital information to diagnose pneumonia, and developed mathematical features and a pattern classifier system suited for the task. We collected cough sounds from 91 patients suspected of acute respiratory illness such as pneumonia, bronchiolitis and asthma. Non-contact microphones kept by the patient's bedside were used for data acquisition. We extracted features such as non-Gaussianity and Mel Cepstra from cough sounds and used them to train a Logistic Regression classifier. We used the clinical diagnosis provided by the paediatric respiratory clinician as the gold standard to train and validate our classifier. The methods proposed in this paper could separate pneumonia from other diseases at a sensitivity and specificity of 94 and 75% respectively, based on parameters extracted from cough sounds alone. The inclusion of other simple measurements such as the presence of fever further increased the performance. These results show that cough sounds indeed carry critical information on the lower respiratory tract, and can be used to diagnose pneumonia. The performance of our method is far superior to those of existing WHO clinical algorithms for resource-poor regions. To the best of our knowledge, this is the first attempt in the world to diagnose pneumonia in humans using cough sound analysis. Our method has the potential to revolutionize the management of childhood pneumonia in remote regions of the world.
肺炎每年在全球范围内导致超过 180 万儿童死亡。这些死亡绝大多数发生在资源匮乏的地区，如撒哈拉以南非洲和偏远亚洲。及时诊断和适当治疗对于预防这些不必要的死亡至关重要。在偏远地区可靠地诊断儿童肺炎面临着许多困难，例如缺乏现场部署的成像和实验室设施以及缺乏经过培训的社区医疗保健工作者。在本文中，我们提出了一种开创性的技术，解决了这两个问题。我们的方法集中于通过不需要与主体物理接触的麦克风自动分析咳嗽和呼吸声音。咳嗽是肺炎的主要症状，但当前在偏远地区使用的临床常规并未利用咳嗽，除了将其作为筛查标准之外。我们假设咳嗽携带诊断肺炎的重要信息，并开发了适合该任务的数学特征和模式分类器系统。我们从 91 名疑似急性呼吸道疾病（如肺炎、细支气管炎和哮喘）的患者中收集了咳嗽声音。使用患者床边的非接触式麦克风进行数据采集。我们从咳嗽声音中提取了非高斯性和梅尔倒谱系数等特征，并使用它们训练逻辑回归分类器。我们使用儿科呼吸临床医生提供的临床诊断作为金标准来训练和验证我们的分类器。基于从咳嗽声音中提取的参数，本文提出的方法可以将肺炎与其他疾病区分开来，敏感性和特异性分别为 94%和 75%。包含其他简单的测量指标，如发热，进一步提高了性能。这些结果表明，咳嗽声音确实携带了有关下呼吸道的关键信息，可以用于诊断肺炎。我们的方法的性能远远优于现有的针对资源匮乏地区的世卫组织临床算法。据我们所知，这是世界上首次尝试使用咳嗽声音分析来诊断人类肺炎。我们的方法有可能彻底改变世界偏远地区儿童肺炎的管理方式。
