Electrocardiogram Recording

Electrocardiogram (ECG) recording focuses on accurately capturing and interpreting the heart's electrical activity to diagnose cardiovascular conditions. Current research emphasizes improving ECG signal processing techniques, particularly using deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), to enhance noise reduction, feature extraction, and diagnostic accuracy. These advancements aim to improve the efficiency and accuracy of ECG analysis, leading to faster diagnoses, better risk stratification, and ultimately improved patient care. Furthermore, research is actively addressing challenges such as data privacy through synthetic data generation and improving the interpretability of complex deep learning models for greater clinical trust and usability.