EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network

TL;DR

This paper introduces a novel two-stage channel-aware Set Transformer network for EEG-based epileptic seizure prediction, reducing sensor count and improving prediction accuracy, validated on the CHB-MIT dataset.

Contribution

The proposed model effectively predicts seizures with fewer EEG channels and introduces a seizure-independent data division method for more rigorous evaluation.

Findings

Average channels reduced from 18 to 2.8 after selection

Sensitivity increased from 76.4% to 80.1% post channel selection

Seizure-independent division improves evaluation rigor

Abstract

Epilepsy is a chronic, noncommunicable brain disorder, and sudden seizure onsets can significantly impact patients' quality of life and health. However, wearable seizure-predicting devices are still limited, partly due to the bulky size of EEG-collecting devices. To relieve the problem, we proposed a novel two-stage channel-aware Set Transformer Network that could perform seizure prediction with fewer EEG channel sensors. We also tested a seizure-independent division method which could prevent the adjacency of training and test data. Experiments were performed on the CHB-MIT dataset which includes 22 patients with 88 merged seizures. The mean sensitivity before channel selection was 76.4% with a false predicting rate (FPR) of 0.09/hour. After channel selection, dominant channels emerged in 20 out of 22 patients; the average number of channels was reduced to 2.8 from 18; and the mean sensitivity rose to 80.1% with an FPR of 0.11/hour. Furthermore, experimental results on the seizure-independent division supported our assertion that a more rigorous seizure-independent division should be used for patients with abundant EEG recordings.