  Hypertension is a medical condition characterized by high blood pressure, and
classifying it into its various stages is crucial to managing the disease. In
this project, a novel method is proposed for classifying stages of hypertension
using Photoplethysmography (PPG) signals and deep learning models, namely
AvgPool_VGG-16. The PPG signal is a non-invasive method of measuring blood
pressure through the use of light sensors that measure the changes in blood
volume in the microvasculature of tissues. PPG images from the publicly
available blood pressure classification dataset were used to train the model.
Multiclass classification for various PPG stages were done. The results show
the proposed method achieves high accuracy in classifying hypertension stages,
demonstrating the potential of PPG signals and deep learning models in
hypertension diagnosis and management.
