Cardiac Arrhythmia

Cardiac arrhythmias, irregular heartbeats, are a major cause of morbidity and mortality, necessitating accurate and timely diagnosis. Current research focuses on leveraging deep learning, particularly convolutional neural networks (CNNs) and novel architectures like capsule networks, to analyze electrocardiogram (ECG) data for improved arrhythmia detection and risk prediction, often incorporating advanced feature extraction techniques. These advancements aim to enhance diagnostic accuracy, reduce reliance on time-consuming manual interpretation, and ultimately improve patient outcomes by enabling earlier and more precise interventions. Furthermore, research is exploring the use of deep learning to analyze other data sources, such as echocardiograms and heart rate variability, to improve arrhythmia classification and risk stratification.