EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network
This work addresses the challenge of wearable seizure prediction for epilepsy patients by enabling prediction with fewer EEG channels, though it is incremental in improving existing methods.
The paper tackled the problem of bulky EEG devices for epilepsy prediction by proposing a two-stage channel-aware Set Transformer Network to reduce the number of sensors needed, achieving a mean sensitivity of 80.1% with a false predicting rate of 0.11/hour after channel selection.
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.