ECG Monitoring System

ECG monitoring systems aim to accurately and efficiently analyze electrocardiogram (ECG) signals to diagnose and monitor cardiovascular health. Current research focuses on developing robust algorithms, often employing deep learning architectures like convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTMs), to classify heartbeats and extract key intervals (e.g., PR, QRS, QT) from single-lead or multi-lead ECG data, even with limited data or noisy signals. These advancements enable the development of smaller, more energy-efficient wearable devices for remote patient monitoring, improving accessibility and potentially reducing healthcare costs, particularly in underserved areas. The ultimate goal is to provide timely and accurate cardiovascular assessments, facilitating early diagnosis and improved patient outcomes.