Continuous Blood Pressure Estimation from TwoChannel PPG Parameters by XGBoost Cuffless and noninvasive continuous blood pressure (BP) measurement is essential for the monitoring of cardiovascular disease. It is widely recognized that the photoplethysmography (PPG) signal can track BP by extracting PPG feature points. In this study, the PPG signal of the two sensors fixed at the hand was extracted, include the timedomain features and the pulse transit time (PTT) features extracted by the twochannel PPG signal. This study proposes a novel feature selection algorithm to ensure continuous extraction of feature points from a lowquality PPG signal. The blood pressure estimator was trained by the XGBoost model, which can solve the problem of missing features. The data was collected from 20 subjects, and a total of 80 sets of data were used to train and validate our proposed model. The results attained the accuracy of 1.56 xc2xb1 3.39 mmHg for SBP and 3.11 xc2xb1 3.78 mmHg for DBP.

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using KNearest Neighbors Algorithm: A Feasibility Study Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classixefxacx81cation accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the Knearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPGxe2x80x93BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual preprocessing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.