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KMID : 1144120230130020221
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Biomedical Engineering Letters
2023 Volume.13 No. 2 p.221 ~ p.233
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Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor
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Seo You-Jung
Kwon Sae-Him
Unang Sunarya
Park Sung-Min
Park Kwang-Suk
Jung Da-Woon
Cho Young-Ho
Park Cheol-Soo
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Abstract
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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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KEYWORD
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Blood pressure, Recalibration, Attention mechanism, Electrocardiogram, Photoplethysmogram, MAE, DNN, Signal processing
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