AI-Based Real-Time Heart Rate (ECG) Monitoring System

Cardiovascular diseases are the number one cause of death in other parts of the world, and prevention has been considered critical for reducing mortality. Current heart monitoring devices, such as electrocardiograms (ECG) or Holter monitors generally suffer from either continuous real-time monitoring deficits, lack of portability and accuracy. In this paper we propose an AI-assisted real-time heart monitoring system which is designed to address those issues. The system integrates state-of-the-art artificial intelligence algorithms with wearable sensors to provide persistent, non-invasive monitoring of heart activity alerting for abnormal events or arrhythmias. Through machine learning models such Convolutional Neural Networks (CNN) and Recurrent Neural networks, they use the data from a pulse oximeter to detect certain irregular heartbeats as well as other cardiac anomalies happening in real time. Wearing a smartwatch with the rules enabled lets an AI sort out heart signals in real time based on how they compare to many thousands it has trained on, notes of course for use-case-specific noise and so forth. They reduce complexity by refining the data (smoothing or filtering noise) and extracting features to provide a clean, valuable dataset for better analysis. These signals are wirelessly transmitted in real time to a central processing unit, where the AI model processes it and immediately provides feedback to patients or clinicians. After testing the system, detection accuracy was improved by more than 95%, and response times were much faster; also a real-time arrhythmia seemed would be detected. Its easy navigation, transportation and automated feedback mechanisms make the system a candidate for an ongoing cardiology support platform that can lighten healthcare facilities' workloads and help accrue patient care with early intervention.