PPG Signals for Hypertension Diagnosis: A Novel Method using Deep Learning Models

TL;DR

This paper presents a novel deep learning approach using PPG signals for non-invasive hypertension stage classification, achieving high accuracy and demonstrating potential for improved diagnosis and management.

Contribution

Introduces a new method combining PPG signals with deep learning (AvgPool_VGG-16) for accurate hypertension stage classification.

Findings

High classification accuracy achieved

Effective use of PPG signals for non-invasive diagnosis

Potential for improved hypertension management

Abstract

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.