Arrhythmia Type

Arrhythmia, characterized by irregular heartbeats, poses significant diagnostic challenges, motivating research into automated detection methods. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) and transformer architectures like ECGformer to analyze electrocardiogram (ECG) data and classify various arrhythmia types (e.g., LBBB, RBBB, PVCs). This focus on automated arrhythmia classification aims to improve diagnostic accuracy, reduce reliance on time-consuming manual interpretation, and ultimately enhance patient care by enabling faster and more reliable diagnoses.