Wearable Device for Blood Pressure Monitoring Based on a Novel Algorithm With a Few Morphological Feature Parameters of Pulse Waves

We proposed an algorithm for blood pressure estimation based on a few morphological feature parameters of pulse waves to solve the problem of over-reliance on high-quality pulse waves (involving numerous pulse wave morphological feature parameters) of blood pressure estimation based on single-channel photoplethysmography (PPG). We successfully implemented the algorithm in a self-developed miniaturized wearable device. Statistical analysis revealed that in the algorithm, a parameter calculated from pulse pressure difference (DP) was highly correlated ( ${R}^{{2}} = {0.942}$ ) with systolic blood pressure (Ps). We defined this parameter as k. We used multiple linear regression to establish a mapping relationship between the morphological feature parameters of pulse waves and DP. This relationship had a correlation coefficient of ${R}^{{2}} = {0.667}$ , ${P}~\lt 0.001$ . We built a miniaturized platform for blood pressure measurement and developed a reflective PPG sensor packaged with polydimethylsiloxane to enable comfortable wearable blood pressure measurement. In uncalibrated randomized experiments, we first evaluated the performance of the algorithm on separate datasets. A total of 116 objects showed a mean absolute error (MAE) of 11.15 and 8.29 mmHg for Ps and diastolic blood pressure (Pd), respectively. In a human experiment with 31 subjects, the errors for Ps and Pd were $5.29~\pm ~10.92$ and $- 2.77~\pm ~8.71$ mmHg, respectively. The results of multistate continuous blood pressure monitoring experiments improved after calibration was performed once. In a prolonged blood pressure monitoring experiment, the errors for the Ps and Pd of subjects were $1.11~\pm ~7.05$ and $0.19~\pm ~2.69$ mmHg, respectively. The results of both experiments conducted in this work were close to the international standard ( $5~\pm ~8$ mmHg) issued by the American Association for the Advancement of Medical Instrumentation. Our algorithm also showed promising results in continuous blood pressure monitoring experiments after exercise and under breath-holding conditions.