Abstract
Current atherosclerosis (AS) assessment devices have a disadvantage for users to carry around. In response to this shortcoming, we propose to collect the wrist photoplethysmograph (PPG) signal and create models to predict the indicators of atherosclerosis (cardiovascular age and right brachial and ankle pulse wave velocity (baPWV)). This study uses the maximum correlation coefficient method for feature selection and establishes multiple models to predict cardiovascular age and the right baPWV. The study results show that the prediction of cardiovascular age using the backpropagation (BP) neural network model is the best. Its Pearson correlation coefficient (PCC) is 0.9501 ( P < 0.05 ), and the model finds the best six physiological features as crest time (CT), crest time ratio (CTR), slop K, stiffness index (SI), reflection index (RI), and heart rate (HR). When predicting the right baPWV value on the right side, we propose a hybrid method MLR_BP, which has better experimental results than BP and MLR. The MLR_BP model improves the prediction accuracy, the predicted PCC value is 0.9204 ( P < 0.05 ), and the model only needs two features, HR and cardiovascular age. This study further verified the results of related literature and proved the relationship between AS and related physiological parameters. The proposed method is applied to wearable devices and has an application value for diagnosing AS and preventing cardiovascular diseases.
Highlights
According to the World Heart Federation report at the World Heart Conference, approximately 20 million people die from various cardiovascular diseases (CVD) each year worldwide
(3) Our study found that heart rate (HR) plays an essential role in predicting right brachial and ankle pulse wave velocity (baPWV), indicating that HR has a specific relationship with AS
Lasso and Ridge Regression’s essence is to add L1 and L2 regularization based on standard linear regression. erefore, two models of Lasso Regression (LR) and RR are used in the experiment
Summary
According to the World Heart Federation report at the World Heart Conference, approximately 20 million people die from various cardiovascular diseases (CVD) each year worldwide. It is estimated that the number of deaths from multiple CVD will exceed 30 million in 2025 [1]. “China Cardiovascular Health and Disease Report 2018” pointed out that China’s CVD prevalence and fatality rate are still on the rise. Arteriosclerosis (AS) is a significant predictor of CVD. It is possible to diagnose AS by magnetic resonance imaging, ultrasound, and other methods clinically. This requires professional equipment, which is high cost, and complicated operation and cannot dynamically obtain AS status at any time. This requires professional equipment, which is high cost, and complicated operation and cannot dynamically obtain AS status at any time. e development of a portable, noninvasive diagnosis of AS wearable devices has positive significance for early screening and diagnosis of CVD
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