  We present the findings of an experimental study whereby we correlate the
changes in the morphology of the photoplethysmography (PPG) signal to healthy
aging. Under this pretext, we estimate the biological age of a person as well
as the age group he/she belongs to, using the PPG data that we collect via a
non-invasive low-cost MAX30102 PPG sensor. Specifically, we collect raw
infrared PPG data from the finger-tip of 179 apparently healthy subjects, aged
3-65 years. In addition, we record the following metadata of each subject: age,
gender, height, weight, family history of cardiac disease, smoking history,
vitals (heart rate and SpO2). We pre-process the raw PPG data to remove noise,
artifacts, and baseline wander. We then construct 60 features based upon the
first four PPG derivatives, the so-called VPG, APG, JPG, and SPG signals, and
the demographic features. We then do correlation-based feature-ranking (which
retains 26 most important features), followed by Gaussian noise-based data
augmentation (which results in 15-fold increase in the size of our dataset).
Finally, we feed the feature set to three machine learning classifiers
(logistic regression, decision tree, random forest), and two shallow neural
networks: a feedforward neural network (FFNN) and a convolutional neural
network (CNN). For the age group classification, the shallow FFNN performs the
best with 98% accuracy for binary classification (3-15 years vs. 15+ years),
and 97% accuracy for three-class classification (3-12 years, 13-30 years, 30+
years). For biological age prediction, the shallow FFNN again performs the best
with a mean absolute error (MAE) of 1.64.
