ECG Analysis

Electrocardiogram (ECG) analysis aims to automatically interpret heart rhythm data for accurate and efficient diagnosis of cardiac conditions. Current research heavily focuses on improving the accuracy and robustness of deep learning models, particularly convolutional neural networks (CNNs) and transformers, often incorporating techniques like self-supervised learning and transfer learning to address data imbalance and artifact issues. These advancements are crucial for enhancing diagnostic capabilities, particularly for rare conditions, and for enabling wider accessibility of ECG-based diagnostics through resource-efficient models suitable for deployment on wearable devices. The ultimate goal is to improve the speed, accuracy, and accessibility of cardiovascular disease diagnosis and management.