Advanced ECG Monitoring: Machine Learning And Robotics For Real-Time Health Management

Cardiovascular diseases are becoming increasingly prevalent due to lifestyle factors such as poor diet, lack of exercise, and conditions like diabetes and hypertension. Electrocardiography (ECG) is a widely used diagnostic tool for detecting various heart conditions, including arrhythmias and myocardial infarction. However, manual analysis of ECG signals is often subjective, time-consuming, and prone to variability. To address these challenges, this paper proposes a comprehensive system that integrates an Arduino-based ECG acquisition module with neural networks for automatic analysis and a robotic system for autonomous sensor placement and intervention. The main objective is to create a smart, real-time ECG monitoring and Computer-Aided Diagnosis (CAD) system capable of early detection and management of heart diseases. The system utilizes machine learning and deep learning techniques to enhance diagnostic accuracy, focusing on implementing a neural network model for ECG classification. A robotic arm is also integrated into the system to ensure precise sensor placement and emergency response