Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram

Fedele, Tommaso; Ramantani, Georgia; Burnos, Sergey; Hilfiker, Peter; Curio, Gabriel; Grünwald, Thomas; Krayenbühl, Niklaus; Sarnthein, Johannes

doi:10.1016/j.clinph.2017.03.038

Cited by 44 publications

(66 citation statements)

References 34 publications

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“…The recent demonstration that a custom low-noise amplifier increased FR-detection rates—and importantly—improved prediction of postoperative outcome is an encouraging development (Fedele et al, 2017). Further technological innovation is clearly needed, especially as it pertains to ongoing efforts to identify FR on scalp EEG (Pizzo et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Intraoperative fast ripples independently predict postsurgical epilepsy outcome: Comparison with other electrocorticographic phenomena

et al. 2017

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In the surgical management of epilepsy, the resection of cortex exhibiting interictal fast ripples (250–500 Hz) on electrocorticography has been linked to postoperative seizure-freedom. Although fast ripples appear to accurately identify the epileptogenic zone—the minimum tissue that must be removed at surgery to achieve seizure-freedom—it has not been established that fast ripples are a superior biomarker in comparison with multimodal presurgical neuroimaging and other electrocorticography abnormalities. Hence, in the prediction of postoperative seizure-freedom, we compared the value of fast ripples with other intraoperative electocorticography abnormalities including focal slowing, paroxysmal fast activity, intermittent spike discharges, continuous epileptiform discharges, focal attenuation, and intraoperative seizures, as well as complete resection of the lesion defined by MRI and other neuroimaging. In a cohort of 60 children with lesional epilepsy and median postsurgical follow-up exceeding 4 years, who underwent resective epilepsy surgery with intraoperative electrocorticography, we evaluated the extent to which removal of each intraoperative electrocorticography abnormality impacts time to first postoperative seizure using the Kaplan-Meier method and Cox proportional hazards regression. Secondly, we contrasted the predictive value of resection of each competing electrocorticography abnormality using standard test metrics (sensitivity, specificity, positive predictive value, and negative predictive value). In contrast with all other intraoperative electrocorticography abnormalities, fast ripples demonstrated the most favorable combination of positive predictive value (100%) and negative predictive value (76%) in the prediction of postoperative seizures. Among all candidate electrocorticography features, time to first postoperative seizure was most strongly associated with incomplete resection of fast ripples (hazard ratio = 19.8, p <0.001). In multivariate survival analyses, postoperative seizures were independently predicted by incomplete resection of cortex generating fast ripples (hazard ratio = 25.4, 95%CI 6.71 – 96.0, p < 0.001) and focal slowing (hazard ratio = 5.79, 95%CI 1.76 – 19.0, p = 0.004), even after adjustment for the impact of an otherwise complete resection. All children with incomplete resection of interictal FR-generating cortex exhibited postoperative seizures within six months. Notably, this cohort included many patients with large resections and thus limited opportunity to exhibit unresected fast ripples. Future study in a cohort with small resection volume, or a clinical trial in which resection margins are guided by fast ripple distribution, would likely yield a more precise estimate of the risk posed by unresected fast ripples. With a high detection rate during brief intraoperative electrocorticography and favorable positive and negative predictive value, interictal fast ripple characterization during surgery is a feasible and useful adjunct to standard methods for epilepsy surgery...

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Section: Discussionmentioning

confidence: 99%

Intraoperative fast ripples independently predict postsurgical epilepsy outcome: Comparison with other electrocorticographic phenomena

et al. 2017

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“…Improved amplifier and analysis settings might overcome these challenges. 26 Cohorts in whom a genetic predisposition results in a likelihood of developing epilepsy in more than 50% of affected individuals, like tuberous sclerosis complex (TSC) or Angelman syndrome, are more promising for biomarker testing. It is already established that the appearance of epileptic spikes heralds the development of epilepsy in TSC, 27 and the same group could also show that HFO are more frequent in patients with TSC than controls.…”

Section: High Frequency Oscillations To Predict Occurrence Of Seizurementioning

confidence: 99%

HFO to Measure Seizure Propensity and Improve Prognostication in Patients With Epilepsy

Jacobs

Zijlmans

2020

Epilepsy Curr

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The study of high frequency oscillations (HFO) in the electroencephalogram (EEG) as biomarkers of epileptic activity has merely focused on their spatial location and relationship to the epileptogenic zone. It has been suggested in several ways that the amount of HFO at a certain point in time may reflect the disease activity or severity. This could be clinically useful in several ways, especially as noninvasive recording of HFO appears feasible. We grouped the potential hypotheses into 4 categories: (1) HFO as biomarkers to predict the development of epilepsy; (2) HFO as biomarkers to predict the occurrence of seizures; (3) HFO as biomarkers linked to the severity of epilepsy, and (4) HFO as biomarkers to evaluate outcome of treatment. We will review the literature that addresses these 4 hypotheses and see to what extent HFO can be used to measure seizure propensity and help determine prognosis of this unpredictable disease.

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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%

“…Presurgical and intraoperative measurement of iEEG signals is often needed to identify the EZ precisely [20][21][22][23] . High Frequency Oscillations (HFOs) have been proposed as a new biomarker in iEEG to delineate the EZ 17,18,[24][25][26][27][28] While HFOs have been historically divided into "ripples" (80-250 Hz) and "fast ripples" (FR, 250-500 Hz), detection of their co-occurrence was shown to enable the optimal prediction of postsurgical seizure freedom 24 . In that study, HFOs were detected automatically by a software algorithm that matched the morphology of the HFO to a predefined template (Morphology Detector) 24,29 An example of such an HFO is shown in Fig.…”

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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Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram

Cited by 44 publications

References 34 publications

Intraoperative fast ripples independently predict postsurgical epilepsy outcome: Comparison with other electrocorticographic phenomena

Intraoperative fast ripples independently predict postsurgical epilepsy outcome: Comparison with other electrocorticographic phenomena

HFO to Measure Seizure Propensity and Improve Prognostication in Patients With Epilepsy

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

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