Electrocardiogram Signal

Electrocardiogram (ECG) signals, representing the heart's electrical activity, are crucial for diagnosing cardiovascular diseases. Current research focuses on improving ECG analysis through advanced signal processing techniques (like denoising and super-resolution) and the application of deep learning models, including convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs) such as LSTMs, often combined with other methods like generative adversarial networks (GANs) or self-supervised learning. These efforts aim to enhance diagnostic accuracy, improve the efficiency of analysis, and enable new applications like automated report generation and anomaly detection. The resulting advancements have significant implications for improving patient care and advancing the field of cardiology.