A new neural network (NN) is orchestrated by this study to achieve high-accuracy in blood pressure (BP) estimation by a real-time photoplethysmography (PPG). The PPG system consists of an OLED/OPD module to detect the pulsation of blood vessels, followed by a readout circuitry. The circuit is comprised of transimpedance amplifier, a digital tune high order band pass filter, programmable gain amplifier (PGA), time interleave OLED driver, micro-controller unit, and the Bluetooth transceiver. The obtained PPG signals are subsequently processed with quality checking, feature extraction, and into an NN for estimating BP. The feature extraction is assisted, by principal component analysis (PCA) to reduce the total number of input features to five with accuracy assured. 96 subjects participated in data collection for calibrating the designed NN. The resulted correlation is 0.81, while the errors for SBP and DBP are 2.00 ± 6.08 and 1.87 ± 4.09 mmHg, respectively. According to the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS), a BP device in Grade A needs to control its accuracy error less than ± 8 mmHg, based on which the BP sensor developed herein are in Grade A, since the resulted errors of ± 6.08 and ± 4.09 mmHg are both less than ± 8 mmHg, showing the satisfactory performance of the BP monitor developed by this study.

这项研究精心设计了一种新的神经网络（NN），以通过实时光体积描记法（PPG）实现高精度的血压（BP）估计。PPG系统由一个OLED / OPD模块组成，以检测血管的搏动，然后是一个读出电路。该电路由互阻放大器，数字调谐高阶带通滤波器，可编程增益放大器（PGA），时间交错OLED驱动器，微控制器单元和蓝牙收发器组成。随后对获得的PPG信号进行质量检查，特征提取并处理到NN中以估计BP。通过主成分分析（PCA）辅助特征提取，以确保准确性将输入特征的总数减少到五个。96名受试者参加了数据收集，以校准设计的NN。结果相关系数为0.81，而SBP和DBP的误差分别为2.00±6.08和1.87±4.09 mmHg。根据医疗器械进步（AAMI）和英国高血压学会（BHS）的要求，A级BP设备需要将其精度误差控制在±8 mmHg以下，基于此，本文开发的BP传感器基于A级，因为结果误差为±6.08和±4.09 mmHg均小于±8 mmHg，表明该研究开发的BP监护仪具有令人满意的性能。