Electrocardiogram Feature

Electrocardiogram (ECG) feature analysis focuses on extracting meaningful information from ECG signals to improve cardiovascular diagnosis and monitoring. Current research emphasizes developing advanced machine learning models, including autoencoders, tree-based methods (like XGBoost), convolutional neural networks (CNNs), and transformer networks, to efficiently process ECG data and identify both cardiac and non-cardiac conditions. This work is significant because it aims to improve diagnostic accuracy, personalize treatment, and enhance patient monitoring, potentially leading to earlier detection of various health issues and improved healthcare outcomes. Furthermore, research also addresses the privacy implications of ECG data and the need for robust anonymization techniques.