Real-Time Anomaly Detection in Wearable Data: A Fuzzy Algorithm Approach for AFib and Bradycardia Monitoring

Resumen

To prevent serious consequences like stroke and heart failure, it is essential to diagnose cardiac arrhythmias like bradycardia and atrial fibrillation early. To identify these circumstances in real time, this work presents a unique monitoring system that combines heart rate, body temperature, and SpO2 data with sophisticated fuzzy algorithms. Remote healthcare apps and continuous tracking are made possible by the system's architecture, which integrates seamless cloud connectivity. Its cutting-edge design guarantees precise diagnosis, prompt action, and better patient results. Combining cognitive algorithms with physiological data analysis, this approach is a major step forward in personalized cardiovascular treatment, improving arrhythmia management accuracy and accessibility.