Electrocardiogram Dataset

Electrocardiogram (ECG) datasets are crucial for developing and validating algorithms that automatically diagnose cardiovascular diseases from ECG signals. Current research focuses on improving the accuracy and efficiency of these algorithms using various deep learning architectures, including convolutional neural networks (CNNs) and transformers, often incorporating novel feature extraction techniques like matrix profile methods or multi-scale approaches to capture both global and local ECG characteristics. This work is driven by the need for faster, more accurate, and accessible cardiac diagnostics, potentially leading to improved patient care and reduced healthcare costs. The development of large, well-annotated datasets and robust model evaluation strategies are key challenges and ongoing areas of focus.