A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG
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 i...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810784/ https://www.ncbi.nlm.nih.gov/pubmed/35110665 http://dx.doi.org/10.1038/s41598-022-05883-8 |
_version_ | 1784644301380124672 |
---|---|
author | Burelo, Karla Ramantani, Georgia Indiveri, Giacomo Sarnthein, Johannes |
author_facet | Burelo, Karla Ramantani, Georgia Indiveri, Giacomo Sarnthein, Johannes |
author_sort | Burelo, Karla |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8810784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88107842022-02-03 A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG Burelo, Karla Ramantani, Georgia Indiveri, Giacomo Sarnthein, Johannes Sci Rep Article 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. Nature Publishing Group UK 2022-02-02 /pmc/articles/PMC8810784/ /pubmed/35110665 http://dx.doi.org/10.1038/s41598-022-05883-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Burelo, Karla Ramantani, Georgia Indiveri, Giacomo Sarnthein, Johannes A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG |
title | A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG |
title_full | A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG |
title_fullStr | A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG |
title_full_unstemmed | A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG |
title_short | A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG |
title_sort | neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp eeg |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8810784/ https://www.ncbi.nlm.nih.gov/pubmed/35110665 http://dx.doi.org/10.1038/s41598-022-05883-8 |
work_keys_str_mv | AT burelokarla aneuromorphicspikingneuralnetworkdetectsepileptichighfrequencyoscillationsinthescalpeeg AT ramantanigeorgia aneuromorphicspikingneuralnetworkdetectsepileptichighfrequencyoscillationsinthescalpeeg AT indiverigiacomo aneuromorphicspikingneuralnetworkdetectsepileptichighfrequencyoscillationsinthescalpeeg AT sarntheinjohannes aneuromorphicspikingneuralnetworkdetectsepileptichighfrequencyoscillationsinthescalpeeg AT burelokarla neuromorphicspikingneuralnetworkdetectsepileptichighfrequencyoscillationsinthescalpeeg AT ramantanigeorgia neuromorphicspikingneuralnetworkdetectsepileptichighfrequencyoscillationsinthescalpeeg AT indiverigiacomo neuromorphicspikingneuralnetworkdetectsepileptichighfrequencyoscillationsinthescalpeeg AT sarntheinjohannes neuromorphicspikingneuralnetworkdetectsepileptichighfrequencyoscillationsinthescalpeeg |