Department of Electrical and Computer Engineering, Tennessee Technological University, Tennessee, United States of America.
Biomed Phys Eng Express. 2021 Oct 20;7(6). doi: 10.1088/2057-1976/ac2ea8.
One of the prominent reasons behind the deterioration of cardiovascular conditions is hypertension. Due to lack of specific symptoms, sometimes existing hypertension goes unnoticed until significant damage happens to the heart or any other body organ. Monitoring of BP at a higher frequency is necessary so that we can take early preventive measures to control and keep it within the normal range. The cuff-based method of measuring BP is inconvenient for frequent daily measurements. The cuffless BP measurement method proposed in this paper uses features extracted from the electrocardiogram (ECG) and photoplethysmography (PPG). ECG and PPG both have distinct characteristics, which change with the change of blood pressure levels. Feature extraction and hybrid feature selection algorithms are followed by a generalized penalty-based regression technique led to a new BP measurement process that uses the minimum number of features. The performance of the proposed technique to measure blood pressure was compared to an approach using an ordinary linear regression method with no feature selection and to other contemporary techniques. MIMIC-II database was used to train and test our proposed method. The root mean square error (RMSE) for systolic blood pressure (SBP) improved from 11.2 mmHg to 5.6 mmHg when the proposed technique was implemented and for diastolic blood pressure (DBP) improved from 12.7 mmHg to 6.69 mmHg. The mean absolute error (MAE) was found to be 4.91 mmHg for SBP and 5.77 mmHg for DBP, which have shown improvement over other existing cuffless techniques where the substantial number of patients, as well as feature selection algorithm, were implemented. In addition, according to the British Hypertension Society standard (BHS) standard for cuff-based BP measurement, the criteria for acceptable measurement are to achieve at least grade B; our proposed method also satisfies this criterion.
导致心血管状况恶化的一个主要原因是高血压。由于缺乏特定的症状，有时现有的高血压在对心脏或任何其他身体器官造成重大损害之前都没有被注意到。需要更频繁地监测血压，以便我们能够采取早期预防措施来控制并将其保持在正常范围内。基于袖带的血压测量方法不便于进行日常频繁测量。本文提出的无袖带血压测量方法使用从心电图（ECG）和光电容积脉搏波（PPG）中提取的特征。ECG 和 PPG 都具有明显的特征，这些特征随血压水平的变化而变化。特征提取和混合特征选择算法之后，采用基于广义惩罚的回归技术，提出了一种新的血压测量过程，该过程使用最少数量的特征。将所提出的血压测量技术的性能与不使用特征选择的普通线性回归方法的方法以及其他当代技术进行了比较。使用 MIMIC-II 数据库对我们提出的方法进行了训练和测试。实施所提出的技术后，收缩压（SBP）的均方根误差（RMSE）从 11.2mmHg 提高到 5.6mmHg，舒张压（DBP）的 RMSE 从 12.7mmHg 提高到 6.69mmHg。对于 SBP，平均绝对误差（MAE）为 4.91mmHg，对于 DBP，MAE 为 5.77mmHg，这表明与其他现有的无袖带技术相比有所改进，这些技术实施了大量患者以及特征选择算法。此外，根据基于袖带的血压测量的英国高血压学会标准（BHS）标准，可接受测量的标准是至少达到 B 级；我们提出的方法也满足这一标准。
