Tiny dLIF: a dendritic spiking neural network enabling a time-domain energy-efficient seizure detection system

Abstract Epilepsy poses a significant global health challenge, driving the need for reliable diagnostic tools like scalp electroencephalogram (EEG), subscalp EEG, and intracranial EEG (iEEG) for accurate seizure detection, localization, and modulation for treating seizures. However, these techniques often rely on feature extraction techniques such as short time Fourier transform (STFT) for efficiency in seizure detection. Drawing inspiration from brain architecture, we investigate biologically plausible algorithms, specifically emphasizing time-domain inputs with low computational overhead. Our novel approach features two hidden layer dendrites with leaky integrate-and-fire spiking neurons, containing fewer than 300 K parameters and occupying a mere 1.5 MB of memory. Our proposed network is tested and successfully generalized on four datasets from the USA and Europe, recorded with different front-end electronics. USA datasets are scalp EEG in adults and children, and European datasets are iEEG in adults. All datasets are from patients living with epilepsy. Our model exhibits robust performance across different datasets through rigorous training and validation. We achieved AUROC scores of 81.0% and 91.0% in two datasets. Additionally, we obtained area under the precision-recall curve and F1 score metrics of 91.9% and 88.9% for one dataset, respectively. We also conducted out-of-sample generalization by training on adult patient data, and testing on children’s data, achieving an AUROC of 75.1% for epilepsy detection. This highlights its effectiveness across continental datasets with diverse brain modalities, regardless of montage or age specificity. It underscores the importance of embracing system heterogeneity to enhance efficiency, thus eliminating the need for computationally expensive feature engineering techniques like fast Fourier transform and STFT.