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TitleApplying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings [electronic resource] / by Thuy T. Pham
ImprintCham : Springer International Publishing : Imprint: Springer, 2019
Connect tohttps://doi.org/10.1007/978-3-319-98675-3
Descript XV, 107 p. 35 illus., 32 illus. in color. online resource

SUMMARY

This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings


CONTENT

Introduction -- Background -- Algorithms -- Point Anomaly Detection: Application to Freezing of Gait Monitoring -- Collective Anomaly Detection: Application to Respiratory Artefact Removals -- Spike Sorting: Application to Motor Unit Action Potential Discrimination -- Conclusion


Biomedical engineering Data mining Engineering Bioinformatics Biomedical Engineering and Bioengineering. http://scigraph.springernature.com/things/product-market-codes/T2700X Data Mining and Knowledge Discovery. http://scigraph.springernature.com/things/product-market-codes/I18030 Computational Intelligence. http://scigraph.springernature.com/things/product-market-codes/T11014 Bioinformatics. http://scigraph.springernature.com/things/product-market-codes/L15001



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