Electrocardiogram Diagnosis

Electrocardiogram (ECG) diagnosis aims to automatically and accurately identify cardiac abnormalities from ECG signals, improving diagnostic efficiency and patient care. Current research heavily focuses on improving the detection of rare cardiac events using deep learning architectures like convolutional neural networks (CNNs), often enhanced with techniques such as self-supervised anomaly detection and multi-view learning, to address the inherent class imbalance in ECG datasets. These advancements, including the incorporation of handcrafted rules and graph neural networks for topological feature extraction, aim to increase diagnostic accuracy and precision, particularly for challenging cases like occlusion myocardial infarction. The ultimate goal is to create more reliable and accessible ECG diagnostic tools for broader clinical use.