Key result
Deep Convolutional Neural Networks analyzing multisite PPG and single-lead ECG predicted subject age with a root mean square error of 7 years, outperforming other feature-based procedures.
Why the study?
Ageing causes arterial stiffness and changes in pulse wave propagation, but whether deep convolutional neural networks analyzing multisite photoplethysmography and electrocardiography can accurately assess cardiovascular ageing remains to be established.
Does a Deep Convolutional Neural Network analyzing multisite PPG and single-lead ECG accurately predict age in a healthy male population?
Observational
Does a Deep Convolutional Neural Network analyzing multisite PPG and single-lead ECG accurately predict age in a healthy male population?
A Deep Convolutional Neural Network using multisite PPG and single-lead ECG can predict age with an RMSE of 7 years, offering a potential non-invasive tool to assess cardiovascular aging and arterial stiffness.
Supports feasibility of CNN-based age prediction from PPG and ECG; leaves open validation in broader populations and clinical utility.
The cardiovascular system is designed to distribute a steady flow through its elastic properties. With ageing, fatigue and fracture of elastin lamellae cause a loss of elasticity defined arterial stiffness. Arterial stiffness causes changes of the pulse wave propagation through the arterial tree, which volumetric counterpart can be assessed non-invasively through photoplethysmography (PPG). PPG may be employed in combination with electrocardiography (ECG). It is here reported an implementation of analysis of multisite PPG and single lead ECG relying on Deep Convolutional Neural Networks (DCNNs). DCNNs generate peculiar filters allowing for data-driven automated selection of the features of interest. The ability of a DCNN to predict subject's age from PPG (left and right brachial, radial and tibial arteries plus fingers) and ECG (Lead I) in a healthy male population (age range: 20-70 years) was investigated. A performance in age prediction of 7 years of root mean square error was obtained, which was superior to other feature-based procedures. The accuracy in age prediction of the machinery in the healthy population may serve for the generation of age-matched normal ranges for the identification of outliers suggesting cardiovascular diseases manifesting as fastened cardiovascular ageing which is recognized as a risk factor for ischemic diseases.
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Chiarelli et al. (2019) conducted an observational in Healthy. Deep Convolutional Neural Networks (DCNNs) analysis of multisite PPG and single lead ECG vs. Other feature-based procedures was evaluated on Age prediction (root mean square error in years). Deep Convolutional Neural Networks analyzing multisite PPG and single-lead ECG predicted subject age with a root mean square error of 7 years, outperforming other feature-based procedures.
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