Datta Shreyasi, Dutta Choudhury Anirban, Deshpande Parijat, Bhattacharya Sakyajit, Pal Arpan
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4594-4598. doi: 10.1109/EMBC.2017.8037879.
Identification of pulmonary diseases comprises of accurate auscultation as well as elaborate and expensive pulmonary function tests. Prior arts have shown that pulmonary diseases lead to abnormal lung sounds such as wheezes and crackles. This paper introduces novel spectral and spectrogram features, which are further refined by Maximal Information Coefficient, leading to the classification of healthy and abnormal lung sounds. A balanced lung sound dataset, consisting of publicly available data and data collected with a low-cost in-house digital stethoscope are used. The performance of the classifier is validated over several randomly selected non-overlapping training and validation samples and tested on separate subjects for two separate test cases: (a) overlapping and (b) non-overlapping data sources in training and testing. The results reveal that the proposed method sustains an accuracy of 80% even for non-overlapping data sources in training and testing.
肺部疾病的识别包括精确听诊以及复杂且昂贵的肺功能测试。现有技术表明，肺部疾病会导致异常肺音，如哮鸣音和湿啰音。本文介绍了新颖的频谱和谱图特征，这些特征通过最大信息系数进一步优化，从而实现健康肺音与异常肺音的分类。使用了一个平衡的肺音数据集，该数据集由公开可用的数据以及使用低成本的内部数字听诊器收集的数据组成。在几个随机选择的非重叠训练和验证样本上验证了分类器的性能，并在两个单独的测试案例中对不同的受试者进行测试：(a) 训练和测试中的重叠数据源；(b) 训练和测试中的非重叠数据源。结果表明，即使在训练和测试中使用非重叠数据源，所提出的方法仍能保持80%的准确率。
