一种基于咳嗽的百日咳自动诊断算法
2016/11/09
百日咳是一种主要影响儿童的传染性呼吸系统疾病，如果不及时治疗可致命。世界卫生组织估计，全球每年有1600万的百日咳病例，导致超过20万人死亡。它主要发生于发展中国家，由于缺乏医疗保健设施和医疗专业人员，此疾病通常难以诊断。因此，在这样的地区，很有必要提供一个低廉、快速、便捷的诊断百日咳的方案以遏制其爆发。在本文中，我们提出了一个算法，利用音频信号通过分析咳嗽和喘息的声音来自动诊断百日咳。该算法包括三个主要模块来进行咳嗽的自动识别、咳嗽的分类和喘息声的检测。对从音频信号中提取的每个相关特征使用逻辑回归模型进行分类。整理输出模块的信息来提供可能诊断百日咳的算法。使用38例患者的音频记录对所提出的算法的性能进行评估。该算法能够成功地从所有的音频录音诊断出所有的百日咳，没有任何误诊。它还可以自动检测个人的咳嗽声，准确率为92%，阳性预测值为97%。该算法并不复杂，再加上其高的精度，表明它可以安装到智能手机上，对百日咳的快速识别或早期筛查和感染爆发控制非常有用。
（苏欣 审校）
PLoS One. 2016 Sep 1;11(9):e0162128. doi: 10.1371/journal.pone.0162128. eCollection 2016.
A Cough-Based Algorithm for Automatic Diagnosis of Pertussis.
Pramono RX1, Imtiaz SA1, Rodriguez-Villegas E1.
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Abstract
Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic coughdetection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control.
PLoS One. 2016 Sep 1;11(9):e0162128. doi: 10.1371/journal.pone.0162128. eCollection 2016.
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