Electrocardiogram Representation

Electrocardiogram (ECG) representation research focuses on efficiently encoding complex ECG signals for improved analysis and downstream applications like arrhythmia detection and disease prediction. Current efforts concentrate on developing effective feature extraction methods using deep learning architectures such as autoencoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), often incorporating self-supervised learning and multimodal approaches that integrate ECG data with clinical text or other medical imaging modalities. These advancements aim to enhance diagnostic accuracy, reduce computational demands, and enable more robust and generalizable ECG-based healthcare solutions.