Detecting anomalies in smart wearables for hypertension: a deep learning mechanism.

ABSTRACT:

Introduction

The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).

Methods

This paper introduces a novel neural network architecture, ResNet-LSTM, to predict BP from physiological signals such as electrocardiogram (ECG) and photoplethysmogram (PPG). The combination of ResNet's feature extraction capabilities and LSTM's sequential data processing offer

SUBMITTER: Kishor Kumar Reddy C 

PROVIDER: S-EPMC11755415 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature