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ProductGraphSleepNet: Sleep staging using product spatio-temporal graph learning with attentive temporal aggregation.

Aref Einizade1, Samaneh Nasiri2, Sepideh Hajipour Sardouie1

  • 1Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.

Neural Networks : the Official Journal of the International Neural Network Society
|May 28, 2023
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Summary

This study introduces ProductGraphSleepNet, an advanced deep learning model for automated sleep stage classification. It improves accuracy by analyzing brain region connections and temporal dynamics, aiding sleep disorder diagnosis.

Keywords:
Brain connectivityGraph convolutional neural (GCN) networkGraph signal processing (GSP)Product graph learning (PGL)Sleep staging

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Automated sleep stage classification is vital for diagnosing sleep pathophysiology.
  • Current deep learning methods often overlook brain region connectivity and temporal dynamics.
  • Subjectivity and time demands of manual sleep scoring necessitate automated solutions.

Purpose of the Study:

  • To propose ProductGraphSleepNet, an adaptive product graph learning-based network for joint spatio-temporal graph learning.
  • To enhance automated sleep staging by modeling inter-epoch connections and brain region interactions.
  • To provide interpretable spatial and temporal connectivity graphs for clinical understanding.

Main Methods:

  • Developed an adaptive product graph learning-based graph convolutional network (ProductGraphSleepNet).
  • Integrated a bidirectional gated recurrent unit and a modified graph attention network.
  • Utilized two public polysomnography datasets (MASS SS3 and SleepEDF) for evaluation.

Main Results:

  • Achieved state-of-the-art performance comparable to existing methods.
  • Reported high accuracy (0.867; 0.838), F1-score (0.818; 0.774), and Kappa (0.802; 0.775) on two databases.
  • Demonstrated the network's ability to generate interpretable connectivity graphs.

Conclusions:

  • ProductGraphSleepNet offers a robust and interpretable approach to automated sleep stage classification.
  • The model effectively captures spatio-temporal dynamics crucial for sleep staging.
  • This advancement holds potential for improved clinical diagnosis and understanding of sleep disorders.