Arrhythmia Detection

Arrhythmia detection, the automated identification of irregular heartbeats from electrocardiograms (ECGs), aims to improve the speed and accuracy of cardiovascular disease diagnosis. Current research emphasizes developing robust and efficient deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and graph convolutional networks (GCNs), often incorporating attention mechanisms and multimodal learning approaches that integrate ECG data with other physiological signals or textual medical information. These advancements are driven by the need for improved diagnostic accuracy, interpretability of model predictions, and the development of resource-efficient algorithms suitable for deployment on wearable devices and low-power microcontrollers for real-time monitoring. The ultimate goal is to enable earlier and more effective intervention for individuals at risk of life-threatening arrhythmias.