Predicting EEG seizures using graded spiking neural networks.

Abstract

Objective.To develop and evaluate a novel, non-patient-specific epileptic seizure prediction system using graded spiking neural networks (GSNNs) implemented on Intel's Loihi 2 neuromorphic processor, addressing the challenges of real-time, energy-efficient prediction to improve patient quality of life.Approach.The GSNN-based system utilized the CHB-MIT dataset for training, integrating hyperparameter optimization, electroencephalogram (EEG) channel selection for data reduction, and a multi-windowed voting mechanism for robustness against noise and artifacts. The system was deployed on Intel's Loihi 2 processor, leveraging its neuromorphic architecture for improved computational efficiency.Main results.The proposed system achieved a non-patient-specific prediction accuracy of 99.14%, outperforming traditional seizure prediction methods. The implementation achieved a throughput of 21.6 EEG segment inputs per second with an energy consumption of 25.104 mJ per input. Additionally, GSNN demonstrated a 6.26 times improvement in event sparsity and a 3.80 times improvement in synaptic communication sparsity compared to artificial neural networks.Significance.This study introduces a robust and energy-efficient GSNN-based framework for epileptic seizure prediction, significantly improving the potential for real-time, wearable applications. By enhancing efficiency and reducing computational complexity, the proposed system demonstrates the substantial promise of GSNNs in advancing neuromorphic computing and addressing critical challenges in epilepsy management.