基于斑点振颤仪的血压评估

Floranne Ellington, Anh Nguyen, Mao-Hsiang Huang, Tai Le, Bernard Choi, Hung Cao
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摘要

连续无创血压 (CNBP) 监测对检测和控制高血压至关重要,而高血压是导致美国人死亡的主要原因。通过利用脉搏到达时间 (PAT)(近端和远端信号峰值之间的时间差)来预测收缩压和舒张压值的开创性方法已得到广泛研究。最广泛使用的配对方法涉及心电图(ECG)和光电血压计(PPG)。在测量血流变化方面,最近研究的一种名为斑点血流超声图(SPG)的光学信号具有类似的特性,与 PPG 相比,SPG 显示出其稳定性和高信噪比。因此,SPG 是与心电图配对进行 CNBP 估测的潜在替代物。本研究旨在挖掘 SPG 作为基于 PAT 的无创血压监测信号的潜力。为了确定 SPG 的功能,八名受试者参加了多次记录会话。心电图和 PPG 测量采用第三方设备,动脉血压 (ABP) 则以商用设备为参考。SPG 测量使用基于智能手机的原型系统进行。在完成坐姿、步行和跑步三个场景后,记录受试者的信号和 ABP,以研究收缩压的预测能力。收集到的数据经过处理,准备用于机器学习模型,包括支持向量回归和决策树回归。使用均方根误差和平均绝对百分比误差评估模型的有效性。在大多数情况下,利用 PATSPG 进行的预测表现出与 PATPPG 相当或更优的性能(例如,在 RSME 中,SPG Rest ± 12.4 mmHg 对 PPG Rest ± 13.7 mmHg;在 MAPE 中,SPG 8% 对 PPG 9%)。此外,加入一个附加特征,即之前的 SBP 值,可减少多个模型配置中两种信号的预测误差(即 RSME 中 SPG Rest ± 12.4 mmHg 降至 ±3.7 mmHg,MAPE 中 SPG Rest 8% 降至 3%)。这些对 SPG 的初步测试凸显了这一新型信号在基于 PAT 的血压预测中的巨大潜力。后续的研究将涉及更多的测试对象和 SPG 采集系统的改进,有望进一步提高这种新探索信号在血压监测中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speckle Plethysmograph-Based Blood Pressure Assessment
Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing PATSPG exhibited comparable or superior performance to PATPPG (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring.
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