Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning

TL;DR

This paper presents a deep learning approach using ResNet and Transformer models to analyze wrist PPG data from smartwatches for reliable, cuffless hypertension risk screening, demonstrating improved accuracy over traditional methods.

Contribution

It introduces a deep learning framework that eliminates handcrafted features for hypertension screening using wrist PPG data, validated on a large longitudinal dataset.

Findings

ResNet outperforms traditional machine learning methods.

Model achieves high accuracy in real-world hypertension detection.

Efficient model with only 0.124M parameters.

Abstract

Hypertension is a leading risk factor for cardiovascular diseases. Traditional blood pressure monitoring methods are cumbersome and inadequate for continuous tracking, prompting the development of PPG-based cuffless blood pressure monitoring wearables. This study leverages deep learning models, including ResNet and Transformer, to analyze wrist PPG data collected with a smartwatch for efficient hypertension risk screening, eliminating the need for handcrafted PPG features. Using the Home Blood Pressure Monitoring (HBPM) longitudinal dataset of 448 subjects and five-fold cross-validation, our model was trained on over 68k spot-check instances from 358 subjects and tested on real-world continuous recordings of 90 subjects. The compact ResNet model with 0.124M parameters performed significantly better than traditional machine learning methods, demonstrating its effectiveness in distinguishing between healthy and abnormal cases in real-world scenarios.