Detecção automatizada de arritmia cardíaca por meio de esp32 e algoritmos de aprendizado de máquina

Abstract

This paper presents the development of an embedded system for real-time cardiac monitoring, integrating machine learning techniques and the Internet of Things. The proposed solution consists of a low-cost prototype based on the ESP32 microcontroller, capable of acquiring elec trocardiogram signals, processing them locally, and displaying the results through a visualization interface. The theoretical foundation considers the application of embedded artificial intelligence in connected systems, highlighting its potential for continuous cardiovascular monitoring and automated detection of heart rhythm abnormalities. The adopted methodology structures the solution into hardware, firmware, and monitoring software layers, enabling signal acquisition, model execution, and real-time data transmission. As a result, the project seeks to provide a functional, documented, and replicable solution that demonstrates the feasibility of integrating embedded devices, machine learning, and data visualization tools. It is concluded that this combination represents a promising alternative for the development of connected health appli cations, with the potential to improve the efficiency of cardiac monitoring.