Electrocardiogram Arrhythmia

Electrocardiogram (ECG) arrhythmia detection focuses on automatically identifying irregular heartbeats from ECG signals, aiming to improve diagnostic accuracy and efficiency compared to manual interpretation. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and transformer architectures, often incorporating attention mechanisms to enhance model interpretability and performance. These advancements are crucial for improving the speed and accuracy of arrhythmia diagnosis, potentially leading to earlier interventions and better patient outcomes in cardiovascular care.