Skip to main content

ORIGINAL RESEARCH article

Front. Public Health
Sec. Digital Public Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1426168
This article is part of the Research Topic Physio-logging in Humans: Recent Advances and Limitations in Wearable Devices for Biomedical Applications View all 4 articles

Detecting Anomalies in Smart Wearables for Hypertension: A Deep Learning Mechanism

Provisionally accepted
Kishor Kumar Reddy C Kishor Kumar Reddy C 1Vijaya Sindhoori Kaza Vijaya Sindhoori Kaza 1R Madana Mohana R Madana Mohana 2Mohammed Alhameed Mohammed Alhameed 3*Fathe Jeribi Fathe Jeribi 3Shadab Alam Shadab Alam 3Mohammed Shuaib Mohammed Shuaib 3
  • 1 Stanley College of Engineering and Technology for Women, Hyderabad, Telangana, India
  • 2 Chaitanya Bharathi Institute of Technology, Hyderabad, Andhra Pradesh, India
  • 3 Jazan University, Jizan, Saudi Arabia

The final, formatted version of the article will be published soon.

    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 offers improved prediction accuracy. Comprehensive error analysis was conducted, and the model was validated using Leave-One-Out (LOO) cross-validation and an additional dataset.The ResNet-LSTM model showed superior performance, particularly with PPG data, achieving a mean absolute error (MAE) of 6.2 mmHg and a root mean square error (RMSE) of 8.9 mmHg for BP prediction. Despite the higher computational cost (~4375 FLOPs), the improved accuracy and generalization across datasets demonstrate the model's robustness and suitability for continuous BP monitoring.The results confirm the potential of integrating ResNet-LSTM into SHM for accurate and non-invasive BP prediction. This approach also highlights the need for accurate anomaly detection in continuous monitoring systems, especially for wearable devices. Future work will focus on enhancing cloud-based infrastructures for real-time analysis and refining anomaly detection models to improve patient outcomes.

    Keywords: deep learning, machine learning, Smart health monitoring, Smart wearables, Hypertension

    Received: 30 Apr 2024; Accepted: 25 Nov 2024.

    Copyright: © 2024 Reddy C, Kaza, Mohana, Alhameed, Jeribi, Alam and Shuaib. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mohammed Alhameed, Jazan University, Jizan, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.