Key result
A backpropagation neural network model using wrist photoplethysmograph signals accurately predicted cardiovascular age and right baPWV.
Why the study?
Current atherosclerosis assessment devices have a disadvantage for users to carry around, prompting the development of wrist photoplethysmograph-based predictive models.
Can wrist photoplethysmograph (PPG) signals combined with machine learning models accurately predict indicators of atherosclerosis such as cardiovascular age and right baPWV?
Observational
Can wrist photoplethysmograph (PPG) signals combined with machine learning models accurately predict indicators of atherosclerosis such as cardiovascular age and right baPWV?
Answers are grounded in this paper and cite their sources.
Wrist photoplethysmograph signals combined with machine learning models can accurately predict cardiovascular age and baPWV, offering a potential wearable solution for non-invasive atherosclerosis assessment.
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.
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Li et al. (2021) conducted an observational in Atherosclerosis. Wrist photoplethysmograph (PPG) signal-based prediction models was evaluated on Prediction of cardiovascular age and right brachial and ankle pulse wave velocity (baPWV) (PCC 0.9501, p=<0.05). A backpropagation neural network model using wrist photoplethysmograph signals accurately predicted cardiovascular age and right baPWV.
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