Biomedical Signal

Biomedical signal processing focuses on extracting meaningful information from physiological signals like ECGs, EEGs, and PPGs to improve healthcare diagnostics and monitoring. Current research emphasizes the development and application of advanced machine learning models, including deep learning architectures like transformers and spiking neural networks, as well as innovative techniques like self-supervised learning and diffusion models, to enhance signal analysis and feature extraction. These efforts aim to improve the accuracy, efficiency, and interpretability of diagnoses for various conditions, ranging from cardiovascular disease to sleep disorders and neurological conditions, ultimately leading to better patient care and more effective clinical decision-making. A key challenge remains ensuring robustness and generalizability across diverse datasets and algorithms.