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Classification between normal and abnormal respiratory sounds based on maximum likelihood approach

机译:基于最大似然法的正常和异常呼吸音之间的分类

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摘要

In this paper, we have proposed a novel classification procedure for distinguishing between normal respiratory and abnormal respiratory sounds based on a maximum likelihood approach using hidden Markov models. We have assumed that each inspiratory/expiratory period consists of a time sequence of characteristic acoustic segments. The classification procedure detects the segment sequence with the highest likelihood and yields the classification result. We have proposed two elaborate acoustic modeling methods: one method is individual modeling for adventitious sound periods and for breath sound periods for the detection of abnormal respiratory sounds, and the other is a microphone-dependent modeling method for the detection of normal respiratory sounds. Classification experiments conducted using the former method revealed that this method demonstrated an increase of 19.1% in its recall rate of abnormal respiratory sounds as compared with the recall rate of a baseline method. It has also been revealed that the latter modeling method demonstrates an increase in its recall rate for the detection of not only normal respiratory sounds but also for abnormal respiratory sounds. These experimental results have confirmed the validity of our proposed classification procedure.
机译:在本文中,我们提出了一种基于隐马尔可夫模型的最大似然方法,用于区分正常呼吸音和异常呼吸音的新分类程序。我们假设每个吸气/呼气周期都由特征性声音片段的时间序列组成。分类程序检测可能性最高的片段序列,并得出分类结果。我们提出了两种精心设计的声学建模方法:一种方法是对不定声音周期和呼吸声周期进行单独建模以检测异常呼吸声,另一种方法是与麦克风相关的建模方法来检测正常呼吸声。使用前一种方法进行的分类实验表明,与基线方法的召回率相比,该方法显示异常呼吸音的召回率提高了19.1%。还已经揭示,后一种建模方法证明其召回率提高,不仅可以检测正常的呼吸音,而且可以检测异常的呼吸音。这些实验结果已经证实了我们提出的分类程序的有效性。

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