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 (dLIF) spiking neurons, containing fewer than 300K 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 AUPRC 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 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 (FFT) and STFT.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Luis Fernando Herbozo Contreras would like to acknowledge the partial support of the Faculty of Engineering Research Scholarship provided by The University of Sydney. Zhaojing Huang would like to acknowledge the support of the Research Training Program (RTP) provided by the Australian Government. Omid Kavehei acknowledges the support provided by The University of Sydney through a SOAR Fellowship and Microsoft support through a Microsoft AI for Accessibility grant

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The TUH dataset is publicly available https://isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml. The EPILEPSIAE dataset is available at cost via this http://www.epilepsiae.eu/project_outputs/european_database_on_epilepsy. The Children's Hospital Boston dataset is publicly available https://physionet.org/content/chbmit/1.0.0. The FB data used in this study used to be available openly via this https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database, but it seems it no longer accepts registration.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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