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

Journal: Frontiers in public health
PMID: 39850864

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).

Authors

  • C Kishor Kumar Reddy
    Stanley College of Engineering and Technology for Women, Hyderabad, India.
  • Vijaya Sindhoori Kaza
    Stanley College of Engineering and Technology for Women, Hyderabad, India.
  • R Madana Mohana
    Department of Artificial Intelligence and Data Science, Chaithanya Bharathi Institute of Technology, Hyderabad, Telangana, India.
  • Mohammed Alhameed
    Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
  • Fathe Jeribi
    Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
  • Shadab Alam
    Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
  • Mohammed Shuaib
    Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.

Keywords

External Resources

Popular Topics
Recent Journals