AUTHOR=Kishor Kumar Reddy C. , Kaza Vijaya Sindhoori , Madana Mohana R. , Alhameed Mohammed , Jeribi Fathe , Alam Shadab , Shuaib Mohammed 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 12 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1426168

DOI=10.3389/fpubh.2024.1426168

ISSN=2296-2565

ABSTRACT=IntroductionThe 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).MethodsThis 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.ResultsThe 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 (~4,375 FLOPs), the improved accuracy and generalization across datasets demonstrate the model’s robustness and suitability for continuous BP monitoring.DiscussionThe 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.