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Collective Anomaly Detection: Application to Respiratory Artefact Removals

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Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings

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Abstract

In this chapter, data sets and feature learning result observations in respiratory artefact removal for lung function tests, specifically the forced oscillation technique (FOT) are presented. The first section introduces the FOT method and respiratory artefacts. The next two sections describe our FOT data sets and performance metrics used to evaluate the proposed scheme in Sect. 5. Section 6 discusses feature selection for FOT data, and two different models for artefact detectors are presented in Sects. 7 and 8. The last four sections reports results/discussion of feature ranking and performance comparison between our proposed detectors and existing methods.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia

    Thuy T. Pham

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  1. Thuy T. Pham
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Correspondence to Thuy T. Pham .

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Pham, T.T. (2019). Collective Anomaly Detection: Application to Respiratory Artefact Removals. In: Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-98675-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-98675-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98674-6

  • Online ISBN: 978-3-319-98675-3

  • eBook Packages: EngineeringEngineering (R0)

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