Biosignal Classification

Biosignal classification uses machine learning to analyze physiological signals like EEG and ECG for diagnostic purposes, aiming to improve accuracy and robustness in healthcare applications. Current research focuses on developing domain-generalization methods to address performance degradation when models encounter data from different sources, employing architectures like transformers and graph neural networks alongside attention mechanisms to better capture complex spatiotemporal relationships within the data. These advancements are crucial for building reliable and widely applicable diagnostic tools, improving the accuracy and trustworthiness of automated disease detection and health monitoring.