Computational Requirements for Hypergraph Neural Networks in Seizure Prediction

5.5 KB of tabular data compares the computational demands of a Hypergraph Neural Network against baseline methods for seizure prediction. The dataset, created by Masoud Amiri and shared on figshare, was last updated in April 2026. It originates from research involving 22 pediatric patients and Kuramoto oscillator dynamics.

Use Cases

• Benchmark training time or memory usage for HP-GNN versus baseline methods using the provided computational metrics.
• Analyze the relationship between model accuracy (e.g., 'Chimera Detection Accuracy', 'Gnn Achieved 84') and computational cost across different frameworks.
• Correlate dataset dimensions ('256 Dimensions') or model complexity ('Layer Hypergraph Convolutions') with the reported computational requirements.
• Validate the efficiency claims of HP-GNN for seizure prediction tasks on the described cohort of 22 pediatric patients.

Strengths

• Data is recent, with a last update in April 2026.
• Provides a direct computational comparison for a specific neural network architecture (HP-GNN).
• Derived from a defined research context involving 22 pediatric patients and 256-dimensional data.

Limitations

• Extremely small data volume at only 5.5 KB, indicating limited scope.
• Unknown row count prevents assessment of statistical robustness.
• Potential bias from a specific, small patient cohort of 22 pediatric subjects.

Provenance

Source

figshare, authored by Masoud Amiri.

Collection Method

Likely generated from experimental evaluation of neural network models.

Freshness

Last updated 2026-04-02.