Purpose

Wrist-cuff oscillometric blood pressure monitors are very popular in the portable medical device market. However, its accuracy has always been controversial. In addition to the oscillatory pressure pulse wave, the finger photoplethysmography (PPG) can provide information on blood pressure changes. A blood pressure measurement system integrating the information of pressure pulse wave and the finger PPG may improve measurement accuracy. Additionally, a neural network can synthesize the information of different types of signals and approximate the complex nonlinear relationship between inputs and outputs. The purpose of this study is to verify the hypothesis that a wrist-cuff device using a neural network for blood pressure estimation from both the oscillatory pressure pulse wave and PPG signal may improve the accuracy.

Design/methodology/approach

A PPG sensor was integrated into a wrist blood pressure monitor, so the finger PPG and the oscillatory pressure wave could be detected at the same time during the measurement. After the peak detection, curves were fitted to the data of pressure pulse amplitude and PPG pulse amplitude versus time. A genetic algorithm-back propagation neural network was constructed. Parameters of the curves were inputted into the neural network, the outputs of which were the measurement values of blood pressure. Blood pressure measurements of 145 subjects were obtained using a mercury sphygmomanometer, the developed device with the neural network algorithm and an Omron HEM-6111 blood pressure monitor for comparison.

Findings

For the systolic blood pressure (SBP), the difference between the proposed device and the mercury sphygmomanometer is 0.0062 ± 2.55 mmHg (mean ± SD) and the difference between the Omron device and the mercury sphygmomanometer is 1.13 ± 9.48 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and the proposed device was 0.28 ± 2.99 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and Omron HEM-6111 was −3.37 ± 7.53 mmHg.

Originality/value

Although the difference in the SBP error between the proposed device and Omron HEM-6111 was not remarkable, there was a significant difference between the proposed device and Omron HEM-6111 in the diastolic blood pressure error. The developed device showed an improved performance. This study was an attempt to enhance the accuracy of wrist-cuff oscillometric blood pressure monitors by using the finger PPG and the neural network. The hardware framework constructed in this study can improve the conventional wrist oscillometric sphygmomanometer and may be used for continuous measurement of blood pressure.

中文翻译：

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一种使用神经网络从振荡压力脉搏波和PPG信号进行血压估计的设备

目的

腕袖式示波血压监测仪在便携式医疗设备市场中非常受欢迎。但是，其准确性一直存在争议。除了振荡压力脉搏波，手指光电容积描记术（PPG）可以提供有关血压变化的信息。集成了压力脉搏波和手指PPG的信息的血压测量系统可以提高测量精度。另外，神经网络可以合成不同类型信号的信息，并近似输入和输出之间的复杂非线性关系。这项研究的目的是验证以下假设，即使用神经网络从振荡压力脉搏波和PPG信号进行血压估计的腕套设备可能会提高准确性。

设计/方法/方法

PPG传感器集成到腕式血压计中，因此在测量过程中可以同时检测手指PPG和振荡压力波。峰值检测后，曲线拟合为压力脉冲幅度和PPG脉冲幅度随时间变化的数据。构建了遗传算法-反向传播神经网络。曲线的参数被输入到神经网络，其输出是血压的测量值。使用水银血压计，带有神经网络算法的发达设备和Omron HEM-6111血压监测仪进行比较，获得了145位受试者的血压测量值。

发现

对于收缩压（SBP），建议的装置与水银血压计之间的差为0.0062±2.55 mmHg（平均值±SD），而Omron装置与水银血压计之间的差为1.13±9.48 mmHg。水银血压计与所提出的装置之间的舒张压差为0.28±2.99 mmHg。水银血压计和Omron HEM-6111之间的舒张压差为-3.37±7.53 mmHg。

创意/价值

尽管所提出的装置与Omron HEM-6111之间的SBP误差差异不明显，但所提出的装置与Omron HEM-6111之间的舒张压误差存在显着差异。开发的设备显示出改进的性能。这项研究是通过使用手指PPG和神经网络来提高腕带示波血压计的准确性的尝试。在这项研究中构建的硬件框架可以改进常规的腕式示波血压计，并且可以用于连续测量血压。