Abstract: Many cardiac rhythm abnormalities develop gradually and may not produce immediate symptoms, making continuous monitoring essential for early identification. This work describes the implementation of a real-time cardiac monitoring system that integrates sensor-based data acquisition, wireless communication, and automated signal analysis. An AD8232 ECG sensor is used to capture cardiac electrical activity, which is processed by an ESP32 controller and transmitted to a cloud platform for storage and visualization. To support ECG analysis, additional parameters such as pulse rate and body temperature are also recorded. A web application developed using the Flask framework retrieves the stored data and applies machine learning models to identify irregular cardiac patterns. Experimental evaluation confirms stable signal acquisition, reliable heart rate computation, and accurate differentiation between normal and abnormal rhythms. The developed system offers a compact, low-cost, and scalable solution suitable for continuous cardiac monitoring in home and remote healthcare environments.

Keywords: ECG analysis, Arrhythmia detection, IoT healthcare, ESP32, AD8232, Cloud-based monitoring, Machine learning.

Downloads: | DOI: 10.17148/IARJSET.2025.121228