Electrocardiogram Classification

Electrocardiogram (ECG) classification aims to automatically diagnose cardiovascular diseases by analyzing ECG signals, improving efficiency and accessibility of cardiac diagnostics. Current research emphasizes developing computationally efficient and robust classification models, focusing on architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, particularly Bi-LSTMs), transformers, and spiking neural networks (SNNs), often incorporating techniques like multi-resolution analysis, multi-feature fusion, and self-supervised learning to enhance accuracy and reduce resource requirements. These advancements hold significant potential for improving the speed and accuracy of cardiac diagnoses, particularly in resource-constrained settings and for large-scale population studies, while also addressing challenges related to data scarcity and model interpretability.