Scalp high-frequency oscillation rates are higher in younger children

Cserpán, Dorottya; Boran, Ece; Biundo, Santo Pietro Lo; Rosch, Richard; Sarnthein, Johannes; Ramantani, Georgia

doi:10.1093/braincomms/fcab052

Cited by 17 publications

(25 citation statements)

References 45 publications

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“…To capture the HFO activity with the highest possible spatial resolution, we re-referenced to a bipolar montage using all combinations of neighboring electrodes, thus obtaining 52 bipolar channels 29 .…”

Section: Methodsmentioning

confidence: 99%

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Scalp HFO rates decrease after successful epilepsy surgery and are not impacted by the skull defect resulting from craniotomy

Cserpán

Gennari

Gaito

et al. 2022

Sci Rep

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Epilepsy surgery can achieve seizure freedom in selected pediatric candidates, but reliable postsurgical predictors of seizure freedom are missing. High frequency oscillations (HFO) in scalp EEG are a new and promising biomarker of treatment response. However, it is unclear if the skull defect resulting from craniotomy interferes with HFO detection in postsurgical recordings. We considered 14 children with focal lesional epilepsy who underwent presurgical evaluation, epilepsy surgery, and postsurgical follow-up of ≥ 1 year. We identified the nearest EEG electrodes to the skull defect in the postsurgical MRI. We applied a previously validated automated HFO detector to determine HFO rates in presurgical and postsurgical EEG. Overall, HFO rates showed a positive correlation with seizure frequency (p < 0.001). HFO rates in channels over the HFO area decreased following successful epilepsy surgery, irrespective of their proximity to the skull defect (p = 0.005). HFO rates in channels outside the HFO area but near the skull defect showed no increase following surgery (p = 0.091) and did not differ from their contralateral channels (p = 0.726). Our observations show that the skull defect does not interfere with postsurgical HFO detection. This supports the notion that scalp HFO can predict postsurgical seizure freedom and thus guide therapy management in focal lesional epilepsy.

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Section: Methodsmentioning

confidence: 99%

“…Scalp HFO detection was conducted with a clinically validated, automated HFO detector 14,[29][30][31][32] that operates in three stages 32 . The detector uses Stockwell's algorithm for the time-frequency transform of the EEG signal 30 .…”

Section: Automated Scalp Hfo Detection In Eegmentioning

confidence: 99%

Scalp HFO rates decrease after successful epilepsy surgery and are not impacted by the skull defect resulting from craniotomy

Cserpán

Gennari

Gaito

et al. 2022

Sci Rep

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“…EEG was recorded while patients took afternoon naps. We selected NREM sleep intervals exclusively since HFO rates are higher during NREM than REM sleep 20 , 33 . Intervals with visible artifacts and channels with continuous interference were excluded from further analysis.…”

Section: Methodsmentioning

confidence: 99%

A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG

Burelo

Ramantani

Indiveri

et al. 2022

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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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“…Nevertheless, the delineation of the EZ using HFO (Frauscher et al, 2017;Thomschewski et al, 2019) has proven to be highly predictive of seizure outcome (Fedele et al, 2017b,c,d;Boran et al, 2019b;Chen et al, 2021;Dimakopoulos et al, 2021) and therefore, may improve the outcome of epilepsy surgery. Moreover, an accumulation of recent evidence suggests that HFO are also measurable by non-invasive scalp EEG (Andrade-Valenca et al, 2011;Boran et al, 2019c;Kuhnke et al, 2019;van Klink et al, 2019;Cserpan et al, 2021aCserpan et al, , 2022aTamilia et al, 2021;Noorlag et al, 2022). Therefore HFO are investigated as potential biomarkers to delineate the EZ but also to monitor disease severity and treatment response (Fan et al, 2020;Jacobs and Zijlmans, 2020).…”

Section: High Frequency Oscillations As a New Biomarker For Epileptog...mentioning

confidence: 99%

Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks

et al. 2022

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Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment.

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Scalp high-frequency oscillation rates are higher in younger children

Cited by 17 publications

References 45 publications

Scalp HFO rates decrease after successful epilepsy surgery and are not impacted by the skull defect resulting from craniotomy

Scalp HFO rates decrease after successful epilepsy surgery and are not impacted by the skull defect resulting from craniotomy

A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG

Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks

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