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Machine Learning Techniques

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The Handbook of Cuffless Blood Pressure Monitoring

Abstract

Driven by the exponential growth in the computational power and the increasing size of the collected data sets, there has been growing interest in using data-driven approaches based on machine learning techniques to resolve the problems and overcome the challenges that facing the area of cuffless blood pressure measurement. Compared with the theory-driven analytical approaches, the machine learning method is very promising with its ability to learn the function of the complex system if the model is trained well, and to address the latent affecting factors that cannot be considered in the analytical model. This chapter first addresses the motivation of employing data-driven method, then provides a brief introduction of machine learning method for cuffless blood pressure estimation. It then presents some of the state-of-the-art examples and applications of such technology and finally discusses the outlook of its future development.

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Authors and Affiliations

  1. Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK

    Xiaorong Ding

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  1. Xiaorong Ding
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Correspondence to Xiaorong Ding .

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Editors and Affiliations

  1. Aktiia SA, Neuchâtel, Switzerland

    Josep Solà

  2. Swiss Center for Electronics and Microtechnology (CSEM, Centre Suisse d’Electronique et de Microtechnique), Neuchâtel, Switzerland

    Ricard Delgado-Gonzalo

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Ding, X. (2019). Machine Learning Techniques. In: Solà, J., Delgado-Gonzalo, R. (eds) The Handbook of Cuffless Blood Pressure Monitoring. Springer, Cham. https://doi.org/10.1007/978-3-030-24701-0_9

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