Liu ZengDing, Zhou Bin, Li Ye, Tang Min, Miao Fen
Chinese Academy of Sciences Key Laboratory for Health Informatics, Shenzhen Institutes of Advanced Technology, Shenzhen, China.
Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Front Physiol. 2020 Sep 9;11:575407. doi: 10.3389/fphys.2020.575407. eCollection 2020.
Continuous blood pressure (BP) provides valuable information for the disease management of patients with arrhythmias. The traditional intra-arterial method is too invasive for routine healthcare settings, whereas cuff-based devices are inferior in reliability and comfortable for long-term BP monitoring during arrhythmias. The study aimed to investigate an indirect method for continuous and cuff-less BP estimation based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals during arrhythmias and to test its reliability for the determination of BP using invasive BP (IBP) as reference.
Thirty-five clinically stable patients (15 with ventricular arrhythmias and 20 with supraventricular arrhythmias) who had undergone radiofrequency ablation were enrolled in this study. Their ECG, PPG, and femoral arterial IBP signals were simultaneously recorded with a multi-parameter monitoring system. Fifteen features that have the potential ability in indicating beat-to-beat BP changes during arrhythmias were extracted from the ECG and PPG signals. Four machine learning algorithms, decision tree regression (DTR), support vector machine regression (SVR), adaptive boosting regression (AdaboostR), and random forest regression (RFR), were then implemented to develop the BP models.
The results showed that the mean value ± standard deviation of root mean square error for the estimated systolic BP (SBP), diastolic BP (DBP) with the RFR model against the reference in all patients were 5.87 ± 3.13 and 3.52 ± 1.38 mmHg, respectively, which achieved the best performance among all the models. Furthermore, the mean error ± standard deviation of error between the estimated SBP and DBP with the RFR model against the reference in all patients were -0.04 ± 6.11 and 0.11 ± 3.62 mmHg, respectively, which complied with the Association for the Advancement of Medical Instrumentation and the British Hypertension Society (Grade A) standards.
The results indicated that the utilization of ECG and PPG signals has the potential to enable cuff-less and continuous BP estimation in an indirect way for patients with arrhythmias.
连续血压（BP）可为心律失常患者的疾病管理提供有价值的信息。传统的动脉内测量方法对于常规医疗环境而言侵入性过大，而基于袖带的设备在可靠性方面较差，且在心律失常期间进行长期血压监测时舒适性欠佳。本研究旨在探讨一种基于心电图（ECG）和光电容积脉搏波描记图（PPG）信号在心律失常期间进行连续且无袖带血压估计的间接方法，并以有创血压（IBP）作为参考来测试其测定血压的可靠性。
本研究纳入了35例接受过射频消融术且临床病情稳定的患者（15例室性心律失常患者和20例室上性心律失常患者）。使用多参数监测系统同时记录他们的心电图、光电容积脉搏波描记图和股动脉有创血压信号。从心电图和光电容积脉搏波描记图信号中提取了15个具有在心律失常期间指示逐搏血压变化潜在能力的特征。然后实施了四种机器学习算法，即决策树回归（DTR）、支持向量机回归（SVR）、自适应增强回归（AdaboostR）和随机森林回归（RFR），以建立血压模型。
结果显示，在所有患者中，采用随机森林回归模型估计的收缩压（SBP）和舒张压（DBP）与参考值相比，均方根误差的平均值±标准差分别为5.87±3.13和3.52±1.38 mmHg，在所有模型中表现最佳。此外，在所有患者中，采用随机森林回归模型估计的收缩压和舒张压与参考值之间的平均误差±误差标准差分别为-0.04±6.11和0.11±3.62 mmHg，符合美国医疗仪器促进协会和英国高血压学会（A级）标准。
结果表明，利用心电图和光电容积脉搏波描记图信号有可能以间接方式为心律失常患者实现无袖带连续血压估计。
