ArticleScientific reports2022
A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 41 citations in OpenAlex.
- Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Zebrafish models of developmental epileptic encephalopathy accurately reflect clinical electrographic biomarkers.Epilepsia · 2026Article
- Wearable Artificial Intelligence for Epilepsy: Scoping Review.Journal of medical Internet research · 2025Article
- The road to commercial success for neuromorphic technologies.Nature communications · 2025Review
- Spiking neural networks for biomedical signal analysis.Biomedical engineering letters · 2024Review
- Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.Nature communications · 2024Article
- Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network.Frontiers in neuroscience · 2023Article
- Scalp HFO rates are higher for larger lesions.Epilepsia open · 2022Article
- Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks.Frontiers in neuroscience · 2022Review
- The present and future of neural interfaces.Frontiers in neurorobotics · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long-term EEG recording. Spiking neural networks (SNN) have emerged as optimal architectures for embedding in compact low-power signal processing hardware. We analyzed 20 scalp EEG recordings from 11 pediatric focal lesional epilepsy patients. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. We identified the optimal SNN parameters to detect EoI and reject artifacts automatically. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.90 CI [0.75 0.96], p < 0.0001, Spearman's correlation). The fully automated SNN detected clinically relevant HFO in the scalp EEG. This study is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.
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