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

• URL: http://arxiv.org/abs/2506.00279v1
• Date: Fri, 30 May 2025 22:21:07 GMT
• Title: Sleep Brain and Cardiac Activity Predict Cognitive Flexibility and Conceptual Reasoning Using Deep Learning
• Authors: Boshra Khajehpiri, Eric Granger, Massimiliano de Zambotti, Fiona C. Baker, Mohamad Forouzanfar,
• Abstract summary: 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.<n>We introduce CogPSGFormer, a multi-scale convolutional-transformer model designed to process multi-modal polysomnographic data.<n>A thorough evaluation of the CogPSGFormer architecture was conducted to optimize the processing of extended sleep signals.
• Score: 7.133591513826875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. To address this, we introduce CogPSGFormer, a multi-scale convolutional-transformer model designed to process multi-modal polysomnographic 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 STAGES 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 Test (PCET) scores. These findings highlight the effectiveness of our multi-scale feature extraction and multi-modal learning approach in leveraging sleep-derived signals for cognitive performance prediction. To facilitate reproducibility, our code is publicly accessible (https://github.com/boshrakh95/CogPSGFormer.git).

Related papers

• Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
  We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
  arXiv  Detail & Related papers  (2025-06-03T14:44:48Z)
• Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework [2.424910201171407]
  This study introduces SynthSleepNet, a multimodal hybrid self-supervised learning framework for analyzing polysomnography (PSG) data.<n> SynthSleepNet effectively integrates masked prediction and contrastive learning to leverage complementary features across multiple modalities.<n>It achieved superior performance compared to state-of-the-art methods across three downstream tasks.
  arXiv  Detail & Related papers  (2025-02-18T10:11:50Z)
• Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks [6.404789669795639]
  In sleep medicine, assessing the evolution of a subject's sleep often involves the costly manual scoring of electroencephalographic (EEG) signals. We present a novel way of integrating learned signal-wise features into said matrices without sacrificing their Symmetric Definite Positive (SPD) nature.
  arXiv  Detail & Related papers  (2024-10-18T06:49:52Z)
• Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification [0.0]
  The present study introduces a Bayesian structure learning algorithm to learn the functional connectivity of EEG in sensor space. The intrasubject (subject-specific) classification performed on 154 subjects for six different verbal working memory loads produced the highest classification accuracy of 96%. The results also show that the alpha and theta bands have better classification accuracy than the beta band.
  arXiv  Detail & Related papers  (2024-04-30T11:31:07Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability [5.747465732334616]
  This study presents an end-to-end deep learning (DL) model which integrates squeeze and excitation blocks within the residual network to extract features and stacked Bi-LSTM to understand complex temporal dependencies. A distinctive aspect of this study is the adaptation of GradCam for sleep staging, marking the first instance of an explainable DL model in this domain with alignment of its decision-making with sleep expert's insights.
  arXiv  Detail & Related papers  (2023-09-10T17:56:03Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters [72.19032452642728]
  We propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset and on a new EEG dataset of unprecedented size. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening.
  arXiv  Detail & Related papers  (2021-10-19T14:22:04Z)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization [0.5091527753265949]
  This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such private data. We use sleep monitoring data from both an open and a large closed clinical study and evaluate whether (1) end-users can create and successfully use customized classification models for sleep apnea detection, and (2) the identity of participants in the study is protected.
  arXiv  Detail & Related papers  (2020-09-21T16:31:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.