Electrocardiogram Analysis

Electrocardiogram (ECG) analysis aims to automatically interpret ECG signals for accurate and efficient diagnosis of cardiovascular diseases. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), often enhanced with attention mechanisms and pre-training techniques such as self-supervised learning and transfer learning to improve performance and interpretability, even with limited labeled data. These advancements are crucial for improving diagnostic accuracy, enabling earlier disease detection, and potentially facilitating personalized medicine through efficient and explainable analysis of ECG data. Furthermore, research is exploring multimodal approaches combining ECG data with other clinical information, such as patient reports, to enhance diagnostic capabilities.