Objective: We study the changes in morphology of the photoplethysmography (PPG) signals-acquired from a select group of South Asian origin-through a low-cost PPG sensor, and correlate it with healthy aging which allows us to reliably estimate the vascular age and chronological age of a healthy person as well as the age group he/she belongs to.
Methods: Raw infrared PPG data is collected from the finger-tip of 173 appar- ently healthy subjects, aged 3-61 years, via a non-invasive low- cost MAX30102 PPG sensor. In addition, the following metadata is recorded for each subject: age, gender, height, weight, family history of cardiac disease, smoking history, vitals (heart rate and SpO2). The raw PPG data is conditioned and 62 features are then extracted based upon the first four PPG derivatives. Then, correlation-based feature-ranking is performed which retains 26 most important features. Finally, the feature set is fed to three machine learning (ML) classifiers, i.e., logistic regression, random forest, eXtreme Gradient Boosting (XGBoost), and two shallow neural networks: a feedforward neural network (FFNN) and a convolutional neural network (CNN).
Main results: For the age group classification problem, the ensemble method XGboost stands out with an accuracy of 99% for both binary classification (3-20 years vs. 20+ years) and three-class classification (3-18 years, 18-23 years, 23+ years). For the vascular/chronological age prediction problem, the ensemble random forest method stands out with a mean absolute error (MAE) of 6.97 years.
Significance: The results demonstrate that PPG is indeed a promising (i.e., low-cost, non-invasive) biomarker to study the healthy aging phenomenon.
Keywords: PPG; chronological age; deep learning; healthy aging; machine learning; vascular age.
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