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Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor

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Abstract

The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.

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Funding

This work was supported in part by the National Research Foundation of Korea (NRF) funded by the Korean Government (MSIT) under Grant NRF-2017R1A5A1015596; and in part by the Commercializations Promotion Agency for R&D Outcomes (COMPA) grant funded by the Korea government(MSIT) (No. 1711179046).

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Author notes

  1. Youjung Seo, Saehim Kwon and Unang Sunarya have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Engineering, Kwangwoon University, Seoul, 01897, Korea

    Youjung Seo, Unang Sunarya & Cheolsoo Park

  2. Department of Artificial Intelligence, Kwangwoon University, Seoul, 01897, Korea

    Saehim Kwon

  3. School of Applied Science, Telkom University, Bandung, 40257, Indonesia

    Unang Sunarya

  4. Department of Convergence IT Engineering and the Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, 37673, Korea

    Sungmin Park

  5. Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, 03080, Korea

    Kwangsuk Park

  6. Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul, 13916, Korea

    Dawoon Jung

  7. Department of Electrical and Communication Engineering, University of Daelim, Anyang, 13916, Korea

    Youngho Cho

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  1. Youjung Seo
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Seo, Y., Kwon, S., Sunarya, U. et al. Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor. Biomed. Eng. Lett. 13, 221–233 (2023). https://doi.org/10.1007/s13534-023-00271-1

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