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A novel method for determination of wheeze type

机译:一种测定喘息型的新方法

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Among respiratory disorders, obstructive diseases such as asthma and chronic obstructive pulmonary disease (COPD) constitute an important group. To our knowledge, there does not exist a study in the literature that quantifies the relationship between the type of wheeze and the type or severity of the disease. This study, aims at classifying wheeze type rather than classical normal-wheeze sound classification studies in the literature. In this study, we propose a method based on Multiple Signal Classification (MUSIC) algorithm to differentiate between monophonic and polyphonic wheezes, without a need for pre-training the algorithm. The algorithm determines the true labels of monophonic and polyphonic wheezes with 100% and 78% accuracy, respectively. Since there does not exist a method in the literature that has been proposed specifically for this problem, only the results of the most relevant few studies have been presented. Since the proposed system can directly estimate the frequency, we consider the method proposed here would be a useful quantification method for further studies in medical literature, on finding correlations between wheezes and disorders.
机译:在呼吸系统障碍中,哮喘和慢性阻塞性肺病(COPD)等阻塞性疾病构成重要组。为了我们的知识,文献中不存在研究,这些研究量化了喘息类型与疾病的类型或严重程度之间的关系。这项研究旨在分类喘息型而不是文献中的古典正常喘息声分类研究。在这项研究中,我们提出了一种基于多信号分类(音乐)算法的方法来区分单声道和多相喘息,而无需预先训练算法。该算法分别确定单声道和多色泻量的真正标签,分别具有100%和78%的精度。由于在文献中没有提出了该问题的文献中不存在的方法,因此仅提出了最相关的少数研究的结果。由于所提出的系统可以直接估计频率,因此我们认为这里提出的方法将是医学文献进一步研究的有用量化方法,用于发现喘息和疾病之间的相关性。

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