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Graph-based semi-supervised one class support vector machine for detecting abnormal lung sounds

Lang, Rongling et al.

Applied mathematics and computation. Volume 364: (2020, January 1st)

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  • Title:
    Graph-based semi-supervised one class support vector machine for detecting abnormal lung sounds
  • Author: Lang, Rongling;
    Lu, Ruibo;
    Zhao, Chenqian;
    Qin, Honglei;
    Liu, Guodong
  • Found In: Applied mathematics and computation. Volume 364: (2020, January 1st)
  • Journal Title: Applied mathematics and computation
  • Subjects: Semi-supervised learning--Abnormal lung sound detection--Support vector machine--Spectral graph; Dewey: 519
  • Rights: Licensed
  • Abstract: Abstract The detection of abnormal lung sounds collected by electronic stethoscopes plays a fundamental role in pulmonary disease diagnostics for primary care and general patient monitoring in telemedicine. Over the past 40 years, the detection has been performed mainly by supervised learning. This method, however, is time- and cost-consuming, and error-prone because it requires manual labeling large numbers of samples. This work proposes a new method, a graph-based semi-supervised one class support vector machine (OCSVM). It can describe normal lung sounds and detect the abnormal ones only by using a small amount of labeled normal samples and abundant unlabeled samples as training samples, which avoids the shortcomings of the traditional methods. A spectral graph is constructed to indicate the relationship of all the samples, which enriches the information provided by only a small number of labeled normal samples. Then, a graph-based semi-supervised OCSVM model is built and its solution is provided. Employing the information in the spectral graph, the proposed method can enhance the effect of recognition and generalization which are crucial for the effective detection of abnormal lung sounds. Finally, the proposed method is evaluated by experiments with all the samples collected in Shijiazhuang, Hebei province, China. The experimental results show that the method outperforms the original OCSVM when the labeled samples are rare. Meanwhile, the performance of the proposed method becomes better as unlabeled abnormal samples increase.
  • Identifier: System Number: ETOCvdc_100088801857.0x000001; Journal ISSN: 0096-3003; 10.1016/j.amc.2019.06.001
  • Publication Date: 2020
  • Physical Description: Electronic
  • Shelfmark(s): 1573.731000
  • UIN: ETOCvdc_100088801857.0x000001

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