Ring and peg electrodes for minimally-Invasive and long-term sub-scalp EEG recordings

Benovitski, Yuri; Lai, Alan; McGowan, Ceara; Burns, Owen; Maxim, V.; Nayagam, David A. X.; Millard, Rodney; Rathbone, Graeme D.; Chevoir, M.A. le; Williams, Richard A.; Grayden, David B.; May, Clive N.; Murphy, Michael A.; D’Souza, Wendyl; Cook, Mark; Williams, Chris

doi:10.1016/j.eplepsyres.2017.06.003

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…For comparison, the median scalp DC-EEG amplitude was one twentieth of the subdural DC-ECoG amplitude (Drenckhahn et al, 2012), whereas the epidural amplitude in the present study was one quarter. The superiority of epidural peg electrodes over scalp EEG electrodes and their inferiority to subdural electrodes is congruent with findings from electrographic seizure detection (Beleza et al, 2010; Benovitski et al, 2017).…”

Section: Discussionsupporting

confidence: 71%

Correlates of Spreading Depolarization, Spreading Depression, and Negative Ultraslow Potential in Epidural Versus Subdural Electrocorticography

et al. 2019

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Spreading depolarizations (SDs) are characterized by near-complete breakdown of the transmembrane ion gradients, neuronal oedema and activity loss (=depression). The SD extreme in ischemic tissue, termed ‘terminal SD,’ shows prolonged depolarization, in addition to a slow baseline variation called ‘negative ultraslow potential’ (NUP). The NUP is the largest bioelectrical signal ever recorded from the human brain and is thought to reflect the progressive recruitment of neurons into death in the wake of SD. However, it is unclear whether the NUP is a field potential or results from contaminating sensitivities of platinum electrodes. In contrast to Ag/AgCl-based electrodes in animals, platinum/iridium electrodes are the gold standard for intracranial direct current (DC) recordings in humans. Here, we investigated the full continuum including short-lasting SDs under normoxia, long-lasting SDs under systemic hypoxia, and terminal SD under severe global ischemia using platinum/iridium electrodes in rats to better understand their recording characteristics. Sensitivities for detecting SDs or NUPs were 100% for both electrode types. Nonetheless, the platinum/iridium-recorded NUP was 10 times smaller in rats than humans. The SD continuum was then further investigated by comparing subdural platinum/iridium and epidural titanium peg electrodes in patients. In seven patients with either aneurysmal subarachnoid hemorrhage or malignant hemispheric stroke, two epidural peg electrodes were placed 10 mm from a subdural strip. We found that 31/67 SDs (46%) on the subdural strip were also detected epidurally. SDs that had longer negative DC shifts and spread more widely across the subdural strip were more likely to be observed in epidural recordings. One patient displayed an SD-initiated NUP while undergoing brain death despite continued circulatory function. The NUP’s amplitude was -150 mV subdurally and -67 mV epidurally. This suggests that the human NUP is a bioelectrical field potential rather than an artifact of electrode sensitivity to other factors, since the dura separates the epidural from the subdural compartment and the epidural microenvironment was unlikely changed, given that ventilation, arterial pressure and peripheral oxygen saturation remained constant during the NUP. Our data provide further evidence for the clinical value of invasive electrocorticographic monitoring, highlighting important possibilities as well as limitations of less invasive recording techniques.

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Section: Discussionsupporting

confidence: 71%

Correlates of Spreading Depolarization, Spreading Depression, and Negative Ultraslow Potential in Epidural Versus Subdural Electrocorticography

et al. 2019

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“…For example, long-term EEG monitoring could enable the identification of changes in network structure or model properties related to changes in medication or dosage. To date, recordings of sufficient duration to capture changes of this nature have not been available to study changes in the dynamics of brain activity over the long periods necessary but will likely soon be possible, for example using subscalp electrodes [18], [19]. These studies ultimately may have prognostic value: revealing patient-specific features of drug efficacy that enable an optimal treatment regime.…”

Section: Discussionmentioning

confidence: 99%

Evolving dynamic networks: An underlying mechanism of drug resistance in epilepsy?

Woldman

Cook

Terry

2019

Epilepsy & Behavior

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At least one-third of all people with epilepsy have seizures that remain poorly controlled despite an increasing number of available anti-epileptic drugs (AEDs). Often, there is an initial good response to a newly introduced AED, which may last up to months, eventually followed by the return of seizures thought to be due to the development of tolerance. We introduce a framework within which the interplay between AED response and brain networks can be explored to understand the development of tolerance. We use a computer model for seizure generation in the context of dynamic networks, which allows us to generate an ‘in silico’ electroencephalogram (EEG). This allows us to study the effect of changes in excitability network structure and intrinsic model properties on the overall seizure likelihood. Within this framework, tolerance to AEDs – return of seizure-like activity – may occur in 3 different scenarios: 1) the efficacy of the drug diminishes while the brain network remains relatively constant; 2) the efficacy of the drug remains constant, but connections between brain regions change; 3) the efficacy of the drug remains constant, but the intrinsic excitability within brain regions varies dynamically. We argue that these latter scenarios may contribute to a deeper understanding of how drug resistance to AEDs may occur.

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“…In addition to generalizing to other diagnostic cases, this deep learning algorithm could also be applied to detect IEDs in other applications that continuously record EEG, such as implantable devices. The development of EEG electrodes that can be worn continuously for longer periods [39], as well as the recent advent of minimally invasive sub-scalp recording devices [40,41] will create a growing demand for automated data review to reliably detect epileptic activity from EEG. At the same time, such devices will ensure an exponentially increasing amount of data becomes available to train deep learning algorithms for IED detection.…”

Section: Patient Total Labelsmentioning

confidence: 99%

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Clarke¹,

Karoly

Nurse

et al. 2021

Epilepsy & Behavior

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Epilepsy diagnosis can be costly, time-consuming and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-EEG monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially herald a more quantitative approach to therapeutic outcomes. There is substantial research into automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic; and, despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG.This study reports on a deep learning algorithm for computer-assisted EEG review. Deep, convolutional neural networks were trained to detect epileptic discharges using a pre-existing dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data was curated and confirmed independently by two epilepsy specialists . The resulting automated detection algorithm was then used to review diagnostic scalp-EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting.The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without .

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Ring and peg electrodes for minimally-Invasive and long-term sub-scalp EEG recordings

Abstract: The results indicate that the peg electrodes provided high quality EEG, mechanical stability, and lower chewing artifact. Whereas, ring electrode arrays tunneled under the scalp enable minimal surgical techniques to be used for implantation and removal.

Cited by 14 publications

References 16 publications

Correlates of Spreading Depolarization, Spreading Depression, and Negative Ultraslow Potential in Epidural Versus Subdural Electrocorticography

Correlates of Spreading Depolarization, Spreading Depression, and Negative Ultraslow Potential in Epidural Versus Subdural Electrocorticography

Evolving dynamic networks: An underlying mechanism of drug resistance in epilepsy?

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

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