English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present the findings of an experimental study whereby we correlate thechanges in the morphology of the photoplethysmography (PPG) signal to healthyaging. Under this pretext, we estimate the biological age of a person as wellas the age group he/she belongs to, using the PPG data that we collect via anon-invasive low-cost MAX30102 PPG sensor. Specifically, we collect rawinfrared PPG data from the finger-tip of 179 apparently healthy subjects, aged3-65 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 thefirst four PPG derivatives, the so-called VPG, APG, JPG, and SPG signals, andthe demographic features. We then do correlation-based feature-ranking (whichretains 26 most important features), followed by Gaussian noise-based dataaugmentation (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 neuralnetworks: a feedforward neural network (FFNN) and a convolutional neuralnetwork (CNN). For the age group classification, the shallow FFNN performs thebest 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 bestwith a mean absolute error (MAE) of 1.64.

A low-cost PPG sensor-based empirical study on healthy aging based on changes in PPG morphology