IoT and Web based Cardiac Arrhythmia System using Machine Learning

Cardiac arrhythmias pose significant health risks and require continuous monitoring for early detection and intervention. In this project, we propose the development of an integrated IoT and web-based system for real-time cardiac arrhythmia detection and monitoring using machine learning techniques. The system comprises an ESP32 microcontroller interfaced with temperature, ECG, and heartbeat sensors, enabling seamless data collection from patients. Collected data is transmitted to the cloud platform ThingSpeak for storage and visualization, facilitating real-time monitoring of vital signs. Concurrently, a machine learning model trained on labeled ECG data is employed to analyze ECG signals for abnormal patterns indicative of arrhythmias. Upon detection of irregularities, the system triggers alerts through Twilio's messaging API, notifying designated recipients for timely intervention. A web interface provides healthcare professionals with remote access to patient data, facilitating comprehensive monitoring and analysis. This project aims to provide an efficient and scalable solution for continuous cardiac arrhythmia monitoring, enhancing patient care and safety.