Detecting anomalies in smart wearables for hypertension: a deep learning mechanism
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 cardiov...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1426168/full |
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| author | C. Kishor Kumar Reddy Vijaya Sindhoori Kaza R. Madana Mohana Mohammed Alhameed Fathe Jeribi Shadab Alam Mohammed Shuaib |
| author_facet | C. Kishor Kumar Reddy Vijaya Sindhoori Kaza R. Madana Mohana Mohammed Alhameed Fathe Jeribi Shadab Alam Mohammed Shuaib |
| author_sort | C. Kishor Kumar Reddy |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1659f16aaf34726ad31db45fce2c5f6 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-e1659f16aaf34726ad31db45fce2c5f62025-01-09T13:31:29ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011210.3389/fpubh.2024.14261681426168Detecting anomalies in smart wearables for hypertension: a deep learning mechanismC. Kishor Kumar Reddy0Vijaya Sindhoori Kaza1R. Madana Mohana2Mohammed Alhameed3Fathe Jeribi4Shadab Alam5Mohammed Shuaib6Stanley College of Engineering and Technology for Women, Hyderabad, IndiaStanley College of Engineering and Technology for Women, Hyderabad, IndiaDepartment of Artificial Intelligence and Data Science, Chaithanya Bharathi Institute of Technology, Hyderabad, Telangana, IndiaDepartment of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi ArabiaDepartment of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi ArabiaDepartment of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi ArabiaDepartment of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi ArabiaIntroductionThe 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.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1426168/fulldeep learningmachine learningsmart health monitoringsmart wearableshypertension |
| spellingShingle | C. Kishor Kumar Reddy Vijaya Sindhoori Kaza R. Madana Mohana Mohammed Alhameed Fathe Jeribi Shadab Alam Mohammed Shuaib Detecting anomalies in smart wearables for hypertension: a deep learning mechanism Frontiers in Public Health deep learning machine learning smart health monitoring smart wearables hypertension |
| title | Detecting anomalies in smart wearables for hypertension: a deep learning mechanism |
| title_full | Detecting anomalies in smart wearables for hypertension: a deep learning mechanism |
| title_fullStr | Detecting anomalies in smart wearables for hypertension: a deep learning mechanism |
| title_full_unstemmed | Detecting anomalies in smart wearables for hypertension: a deep learning mechanism |
| title_short | Detecting anomalies in smart wearables for hypertension: a deep learning mechanism |
| title_sort | detecting anomalies in smart wearables for hypertension a deep learning mechanism |
| topic | deep learning machine learning smart health monitoring smart wearables hypertension |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1426168/full |
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