A wearable ECG and PPG sensor using machine learning accurately estimated vascular age (r=0.89) and 10-year cardiovascular disease risk (r=0.9488) compared to a gold-standard instrument.
Observational (n=501)
Does a wearable sensor using ECG, PPG, and machine learning accurately estimate vascular age and cardiovascular disease risk compared to a gold standard instrument in diverse subjects?
A novel wearable sensor combining ECG, PPG, and machine learning algorithms can accurately estimate vascular age and 10-year cardiovascular disease risk, offering potential for home-care arterial stiffness monitoring.
Effect estimate: r=0.89 (vascular age), r=0.9488 (CVD risk)
Arterial stiffness is strongly associated with cardiovascular events. Existing devices for evaluating arterial stiffness based on ultrasound or pulse wave velocity suffer a lot from complexity and inconvenience in home-care settings. This paper proposed a wearable sensor for arterial stiffness monitoring via machine learning techniques. The proposed sensor is comprised of one electrocardiogram (ECG) and one photoplethysmogram (PPG) module. The ECG and PPG signals were first simultaneously collected by the wearable sensor, and 21 features were extracted from two signals for arterial stiffness evaluation. A genetic algorithm-based feature selection method was then used to select the important indicators. Multivariate linear regression (MLR), decision tree, and back propagation (BP) neural network were employed to develop the model. Vascular age and 10-year cardiovascular disease risk from OMRON arteriosclerosis instrument were deemed as the gold standard to evaluate arterial stiffness. Experimental results based on 501 diverse subjects showed that the MLR approach exhibited the best accuracy in vascular age estimation (correlation coefficient, 0.89; mean of the residual, 0.2136; and standard deviation of the residual, 6.2432). While the BP neural networks-based approach was best in cardiovascular disease risk estimation (correlation coefficient, 0.9488; mean of the residual, - 0.3579%; and standard deviation of the residual, 3.7131%). The results indicate that the proposed learning-based sensor has great potential in arterial stiffness monitoring in home-care settings.
Miao et al. (Mon,) conducted a observational in Arterial stiffness (n=501). Wearable ECG and PPG sensor with machine learning vs. OMRON arteriosclerosis instrument was evaluated on Vascular age and 10-year cardiovascular disease risk estimation (r=0.89 (vascular age), r=0.9488 (CVD risk)). A wearable ECG and PPG sensor using machine learning accurately estimated vascular age (r=0.89) and 10-year cardiovascular disease risk (r=0.9488) compared to a gold-standard instrument.