Tag
This paper presents a comprehensive comparison of deep learning architectures, including Vision Transformers and Graph Attention Networks, for automated sleep apnea detection from multichannel EEG signals, achieving a best test AUC of 0.750 using a vision transformer trained on topological data analysis features.
This paper presents a longitudinal multimodal study of 66 older adults using wearable sensing and clinical assessments to predict physical activity, sleep duration, and sleep apnea severity, finding that behavioral targets are more predictable and historical features are key predictors.