Seo Youjung, Kwon Saehim, Sunarya Unang, Park Sungmin, Park Kwangsuk, Jung Dawoon, Cho Youngho, Park Cheolsoo
Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Korea.
Department of Artificial Intelligence, Kwangwoon University, Seoul, 01897 Korea.
Biomed Eng Lett. 2023 Mar 23;13(2):221-233. doi: 10.1007/s13534-023-00271-1. eCollection 2023 May.
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.
可穿戴技术在医疗保健领域的迅速发展为人们在日常活动中的任何时间使用智能手表测量血压创造了机会。最近，几款市面上可买到的可穿戴设备都配备了血压监测功能。然而，重新校准的问题仍然存在。初始校准需要基于脉搏传输时间（PTT）的估计，随后是定期重新校准。在日常活动中使用臂式袖带血压监测仪进行重新校准并不实际。在本研究中，我们研究了借助深度神经网络（DNN）进行基于PTT的血压监测的重新校准，并验证了使用更实用的腕式袖带血压监测仪所取得的性能。当使用采用重新校准的臂式袖带监测仪进行测试时，基于PTT的预测对于收缩压（SBP）产生的平均绝对误差（MAE）为4.746±1.529 mmHg，对于舒张压（DBP）为3.448±0.608 mmHg。重新校准明显提高了DNN和传统线性回归方法的性能。我们确定，腕戴式血压监测仪进行的定期重新校准可以与臂戴式监测仪获得的校准一样准确，证实了腕戴式设备适用于日常使用。这是第一项确定腕式袖带血压监测仪作为一种校准血压监测设备的手段的潜力的研究，这种设备可以可靠地替代臂式袖带血压监测仪。通过使用腕式袖带血压监测设备，连续血压估计以及为确保准确血压监测而进行的频繁校准现在都可行了。
