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NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.

  • Updated Aug 7, 2024
  • Jupyter Notebook

The W4H Integrated Toolkit Repository provides a unified platform for managing, analyzing, and visualizing wearable health data using a suite of open-source tools and frameworks.

  • Updated May 15, 2024
  • Python

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