Use CPET data to predict the intervention effect of aerobic exercise on young hypertensive patients The incidence of hypertension has recently shown a significant increase in young people, with aerobic exercise intervention being recognized as an effective approach to decrease blood pressure (BP). However, BP response to aerobic exercise can be highly individualized, and no research has been conducted on predicting the effect of aerobic exercise intervention for reducing BP in young hypertensive patients. In this work, we use the data generated from a cardiopulmonary exercise test (CPET) in young hypertensive patients (before aerobic exercise intervention) to derive information from multiple cardiopulmonary metabolic indices. The data, presented as time series, are then analyzed by a machine learning method to predict the effect of aerobic exercise intervention in lowering BP. This study provides several novel insights for making personalized aerobic exercise intervention programs for young adults with stage I hypertension.

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.