Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning

-- Despite extensive research on the relationship between sleep and cognition, the connection between sleep microstructure and human performance across specific cognitive domains remains underexplored. This study investigates whether deep learning models can predict executive functions, particularly cognitive adaptability and conceptual reasoning from physiological processes during a night's sleep. T o address this, we introduce CogPSGFormer, a multi-scale convolutional-transformer model designed to process multi-modal polysomno-graphic data. This model integrates one-channel ECG and EEG signals along with extracted features, including EEG power bands and heart rate variability parameters, to capture complementary information across modalities. A thorough evaluation of the CogPSGFormer architecture was conducted to optimize the processing of extended sleep signals and identify the most effective configuration. The proposed framework was evaluated on 817 individuals from the ST AGES dataset using cross-validation. The model achieved 80.3% accuracy in classifying individuals into low vs. high cognitive performance groups on unseen data based on Penn Conditional Exclusion T est (PCET) scores. I. INTRODUCTION Cognitive decline linked to changes in sleep characteristics--such as variations in sleep architecture, quality, and duration--represents a significant global health challenge.