Ku Chin-Jung, Wang Yuhling, Chang Chia-Yu, Wu Min-Tse, Dai Sheng-Tong, Liao Lun-De
Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, No 35, Keyan Road, Zhunan Town, Miaoli County 350, Taiwan.
Heliyon. 2022 Nov 18;8(11):e11698. doi: 10.1016/j.heliyon.2022.e11698. eCollection 2022 Nov.
The popularization of long-term invasive tools for continuously monitoring blood pressure remains challenging. However, with the rising popularity of wearable personal health management devices, non-cuff blood pressure measurement technology that applies electrocardiography (ECG) and photoplethysmography (PPG) has gradually received increasing attention. In particular, whether blood pressure can be measured continuously by the PPG signal alone is of great interest. In this study, we aim to develop a device that includes systolic and diastolic blood pressure calculation formulas derived from characteristic waveform points in the PPG time domain and that can measure blood oxygenation and heart rate. This device applies empirical formulas developed by PPG waveforms in the PhysioNet MIMIC-II database to calculate blood pressure. The systolic and diastolic pressures are then compared with the actual blood pressures obtained from invasive blood pressure waveforms to verify the effectiveness and feasibility of the complete developed system. Overall, 263 waveforms with double peaks and 261 waveforms with only a single peak totaling 524 sets of data are used to derive the empirical formulas. The systolic blood pressure estimation result using single peak analysis has an excessively large error exceeding ±40 mmHg, providing no reference value. However, systolic blood pressure estimation is notably better in double peak analysis, with error values reducing to approximately 23 mmHg. Diastolic pressure estimation errors are low with both single (±7 mmHg) and double peak (±4 mmHg) analyses. The error is lower in double-peak analysis than in single-peak analysis for obtaining systolic pressure from PPG waves. We plan to use PPG to detect additional physiological parameters in the future, e.g., respiratory rate, heart rate variability, or irregular heartbeat, to further enhance the functionality of PPG-based wearable devices.
长期侵入式血压连续监测工具的普及仍然具有挑战性。然而，随着可穿戴个人健康管理设备的日益普及，应用心电图（ECG）和光电容积脉搏波描记法（PPG）的非袖带血压测量技术逐渐受到越来越多的关注。特别是，仅通过PPG信号能否连续测量血压引起了极大的兴趣。在本研究中，我们旨在开发一种设备，该设备包括从PPG时域中的特征波形点导出的收缩压和舒张压计算公式，并且能够测量血氧饱和度和心率。该设备应用PhysioNet MIMIC-II数据库中由PPG波形开发的经验公式来计算血压。然后将收缩压和舒张压与从侵入性血压波形获得的实际血压进行比较，以验证整个开发系统的有效性和可行性。总体而言，使用了263个双峰波形和261个单峰波形，共524组数据来推导经验公式。使用单峰分析的收缩压估计结果误差过大，超过±40 mmHg，没有参考价值。然而，在双峰分析中收缩压估计明显更好，误差值降至约23 mmHg。单峰（±7 mmHg）和双峰（±4 mmHg）分析的舒张压估计误差都较低。从PPG波获取收缩压时，双峰分析中的误差低于单峰分析。我们计划在未来使用PPG检测更多生理参数，例如呼吸频率、心率变异性或心律失常，以进一步增强基于PPG的可穿戴设备的功能。
