aceEpileptic seizure onset early warning using a lightweight multimodal self-explainable neural network
BackgroundCutting-edge deep learning algorithms are applied to epileptic seizures to uncover hidden patterns and achieve high accuracy. Post hoc algorithms explain black-box models but do not reveal the detailed workings of the algorithms. Therefore, intrinsic methods overcome these issues; however, they suffer from improper use of indicative metrics and performance-to-accuracy trade-offs.ObjectiveThis study implements a lightweight multimodal explainable Neural Network for real-time early warning of epileptic s…
Two self-explainable neural network models trained on EEG from drug-resistant epilepsy patients detected seizure onset with 95-97% accuracy while exposing how predictions are made.
Evidence
- Peer-reviewedTechnology and Health Care2026-10-16
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Truvace Impact Record TRV-2026-1947, v1: “Epileptic seizure onset early warning using a lightweight multimodal self-explainable neural network.” Truvace, 2026-10-19. /record/TRV-2026-1947 (accessed at citation time). sha256 f2760f530b9fb3e0…
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