Liquid-Dendrite Spiking Neural Network for Edge Devices: A 130 K-Parameter, 535 KB Model for Time-Domain Epileptic Seizure Detection

Epilepsy is a significant global health issue, requiring dependable diagnostic tools like scalp encephalogram (scalp-EEG), sub-scalp EEG, and intracranial EEG (iEEG) for precise seizure detection and treatment. AI has emerged as a powerful tool in this domain, offering the potential for real-time, responsive monitoring. Traditional methods often rely on feature extraction techniques like Short-Time Fourier Transform (STFT), which can increase power consumption, making them less suitable for deployment on edge devices. While large models can improve accuracy without STFT, their size also limits their practicality for edge applications. This study introduces Liquid-Dendrite, a novel bio-inspired model for seizure detection, leveraging Liquid-Time Constant Spiking Neurons (LTC-SN) and dendrites spiking neurons (dSN) with heterogeneous time-constants. The model comprises two hidden layers with dendritic neurons and one layer of liquid-time constant networks. Our model achieves a memory efficacy of 535 KB with 130 K trainable parameters. The model was tested across the most noteworthy epilepsy datasets for scalp EEG (TUH and CHB-MIT) and iEEG (EPILEPSIAE). Our model demonstrated commendable performance, achieving AUROC scores of 83%, 96%, and 93%, respectively, outperforming some existing models in an energy and memory-efficient way. Moreover, we conducted a robustness test by blacking out EEG channels at the inference stage, where we showed the ability of our network to work with fewer channels. We could deploy our tiny model and perform inference at the edge of the Raspberry Pi 5 without the need for additional quantization. This highlights the potential of Neuro-Inspired AI for efficient, small-scale, and energy-embedded AI systems across different brain modalities.