Single Lead Electrocardiogram Data

Single-lead electrocardiogram (ECG) data analysis focuses on extracting meaningful information from the heart's electrical activity using a single lead, offering a less invasive and more accessible alternative to traditional 12-lead ECGs. Current research emphasizes improving the accuracy of arrhythmia detection and risk prediction using deep learning models, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often incorporating techniques like self-supervised learning and transfer learning to address data limitations and enhance model interpretability. These advancements hold significant promise for improving the efficiency and accessibility of cardiovascular diagnostics, particularly in remote monitoring and resource-constrained settings, and for enabling more personalized and proactive healthcare.