High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome

Boran, Ece; Ramantani, Georgia; Krayenbühl, Niklaus; Schreiber, Maxine; König, Kristina; Fedele, Tommaso; Sarnthein, Johannes

doi:10.1016/j.clinph.2019.07.008

Cited by 47 publications

(81 citation statements)

References 26 publications

(41 reference statements)

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“…FR detected with electrode strips of 10 mm contact spacing have proven sufficient for outcome prediction in several past studies 2,3,20–23 . High-density electrode grids with 5 mm contact spacing have been recently shown to improve FR detection and outcome prediction 23 . The ECoG coverage and spatial sampling were thus sufficient for intraoperative FR detection in our study.…”

Section: Methodsmentioning

confidence: 90%

High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy

Boran

Sarnthein

Krayenbühl

et al. 2019

Sci Rep

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High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2 = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.

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Section: Methodsmentioning

confidence: 90%

High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy

Boran

Sarnthein

Krayenbühl

et al. 2019

Sci Rep

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“…Bio-signal recording headstages typically comprise analog circuits to amplify and filter the signals being measured, and can be highly diverse in specifications depending on the application 11 . For example, neural recording headstages for experimental neuroscience target high-density recordings [12][13][14][15] and minimize the circuit area requirements, while devices used for clinical studies and therapeutic applications require a small number of recording channels and the highest possible signal-to-noise ratio (SNR) [16][17][18][19] In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core SNN architecture on the same die for recording, processing, and detecting clinically relevant biomarkers in intracranial EEG recordings (iEEG) from epilepsy patients.…”

Section: Introductionmentioning

confidence: 99%

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

Sharifhazileh

Burelo

Sarnthein³

et al. 2020

Preprint

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The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from compact and portable devices that can process these signals locally, in real-time, without the need for off-line processing. An example is the recording of intracranial EEG(iEEG) during epilepsy surgery with the detection of High Frequency Oscillations (HFOs, 80-500 Hz), which are a biomarker for the epileptogenic zone. Conventional approaches of HFO detection involve the offline analysis of prerecorded data, often on bulky computers. However, clinical applications during surgery or in long-term intracranial recordings demand a self-sufficient embedded device that is battery-powered to avoid interfering with other electronic equipment in the operation room. Mixed-signal and analog-digital neuromorphic circuits offer the possibility of building compact, embedded, and low-power neural network processing systems that can analyze data on-line and produce results with short latency in real-time. These characteristics are well suited for clinical applications that involve the processing of biomedical signals at (or very close to) the sensor level. In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting clinically relevant HFOs in iEEG from epilepsy patients. The device was fabricated using a standard 0.18μm CMOS technology node and has a total area of 99 mm2. We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent epilepsy surgery. The total average power consumption of the chip during the detection task was 614.3 μW. We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity, and sensitivity (78%, 100%, and 33% respectively). This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and spiking neural networks. By providing “neuromorphic intelligence” to neural recording circuits the approach proposed will pave the way for the development of systems that can detect HFO areas directly in the operation room and improve the seizure outcome of epilepsy surgery.

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“…In intraoperative ECoG, the primary purpose of HFO detection is the delineation of the EZ, i.e. the prediction of recurrent seizures if the EZ has not been entirely resected [7][8][9][10][11][12][13][14][15][16] . As a fundamental problem, HFOs have been defined non-uniformly across research studies 7 .…”

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confidence: 99%

“…HFO detection faces the challenge of low signal-to-noise ratio, which can be improved by high density electrodes 8,20 and by low-noise amplification 11 . As a further challenge, a clinically relevant HFO must be distinguished from the electrical artifacts induced by the standard intraoperative devices or any other spurious oscillation in the fast ripple band.…”

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confidence: 99%

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A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG

Burelo

Sharifshazileh

Krayenbühl

et al. 2021

Sci Rep

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To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative electrocorticography (ECoG) recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset (58 min, 16 recordings). We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (Spearman’s $$\rho$$ ρ = 0.81). The postsurgical seizure outcome was “predicted” with 100% (CI [63 100%]) accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone.

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High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome

Abstract: OBJECTIVES: Residual fast ripples (FR) in the intraoperative ECoG are highly specific predictors of postsurgical seizure recurrence. However, a FR is generated by a small patch of cortical tissue. Spatial sampling with standard electrodes may thus miss clinically relevant information.

Cited by 47 publications

References 26 publications

High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy

High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG

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