An energy-efficient dual-branch spiking neural network for epileptic seizure detection from electroencephalogram signals.

Abstract

Epileptic seizure detection from electroencephalogram (EEG) signals is critical for clinical diagnosis and long-term neurological monitoring. However, conventional artificial neural networks (ANNs) are often computationally expensive and energy demanding, which hinders their deployment in large-scale or real-time brain-signal analysis. Spiking neural networks (SNNs) provide a biologically inspired and energy-efficient alternative, yet existing architectures still struggle to balance accuracy and efficiency in EEG-based seizure detection. In this study, we propose an adaptive integrate-and-fire (AIF) spiking neuron model that dynamically adjusts its temporal behavior to capture diverse activation patterns. Based on this neuron, we develop a dual-branch spiking neural network (DBSNet), designed to decode multi-scale and multi-dimensional EEG features for improved seizure detection. We evaluate DBSNet on three public epileptic EEG datasets. Among SNN-based approaches, DBSNet consistently achieves state-of-the-art performance. On a large-scale dataset, it even surpasses the best-performing ANN while consuming only one-seventh of its theoretical energy, highlighting its efficiency advantage. These results demonstrate the potential of adaptive spiking architectures to achieve accurate and sustainable neural computing for EEG-based seizure detection, and they suggest a promising paradigm for broader applications in brain-signal processing.