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Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography

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Neural Information Processing (ICONIP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12534))

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Abstract

Pulse transit time (PTT) provides a cuffless method to measure and predict blood pressure, which is essential in long term cardiac activity monitoring. Photoplethysmography (PPG) sensors provide a low-cost and wearable approach to obtain PTT measurements. The current approach to calculating PTT relies on quasi-periodic pulse event extractions based on PPG local signal characteristics. However, due to inherent noise in PPG, especially at uncontrolled settings, this approach leads to significant errors and even missing potential pulse events. In this paper, we propose a novel approach where global features (all samples) of the time-series data are used to develop a machine learning model to extract local pulse events. Specifically, we contribute 1) a new noise resilient machine learning model to extract events from PPG and 2) results from a study showing accuracy over state of the art (e.g. HeartPy) and 3) we show that MLPTT outperforms HeartPy peak detection, especially for noisy photoplethysmography data.

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Acknowledgements

This work was supported by the University of Sydney Cardiovascular Initiative funding. Dr Withana is the recipient of an Australian Research Council Discovery Early Career Award (DE200100479) funded by the Australian Government.

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Authors and Affiliations

  1. School of Computer Science, The University of Sydney, Sydney, NSW, 2008, Australia

    Philip Mehrgardt, Matloob Khushi, Anusha Withana & Simon Poon

  2. Cardiovascular Initiative, The University of Sydney, Sydney, NSW, 2008, Australia

    Anusha Withana

Authors
  1. Philip Mehrgardt
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  2. Matloob Khushi
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  3. Anusha Withana
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  4. Simon Poon
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Corresponding author

Correspondence to Philip Mehrgardt .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Mehrgardt, P., Khushi, M., Withana, A., Poon, S. (2020). Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-63836-8_5

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  • Online ISBN: 978-3-030-63836-8

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