Pressure-Guard: Smart Real-Time Blood Pressure Prediction and Monitoring System | ||||
| Port-Said Engineering Research Journal | ||||
| Articles in Press, Accepted Manuscript, Available Online from 09 July 2025 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/pserj.2025.393653.1416 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Hala Samir Elhadidy   1; Salma Sherif2; Mennatullah Azab2; Merna Elbolaky2; Walaa Saber2; Mohamed Said2; Amr Elsaid2
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| 1Faculty of Engineering, Port Said University | ||||
| 2Electrical Engineering Department, Faculty of Engineering, Port Said University | ||||
| Abstract | ||||
| Blood pressure(BP) is a vital physiological indicator that is impacted by age, body mass index (BMI), and stress levels. Although traditional methods provide accurate measurements, they are impractical for continuous monitoring. Pressure-Guard is an intelligent system designed for continuous BP measurement using Artificial Intelligent (AI). The proposed model can send immediate alerts if the pressure is out of the safe levels in order to prevent health setbacks when patients are alone. The proposed model aims to estimate BP using Electrocardiogram (ECG) and Photoplethysmography (PPG) signals. While both Machine Learning (ML) and Deep Learning (DL) could be used for this task, DL is chosen due to the nature of the data and the complexities involved. The dataset, which is used in training AI model, is obtained from the Non-Invasive BP dataset, available at Kaggle. The continuous readings from two key biosensors, which are used in BP estimation via the training model, are used for monitoring BP. A mobile App is developed to monitor BP with key features such as: sending alerts if BP exceeds normal range, emergency notifications sent to companions, a locator for doctors and hospitals, a video call feature between patients and companions, medicine reminders for timely medication intake and correct dosage, a symptom checker to suggest potential conditions. | ||||
| Keywords | ||||
| Electrocardiogram (ECG); Diastolic Blood Pressure (DBP); Long short time memory (LSTM); Photoplethysmography (PPG) | ||||
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