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

AbstractBackgroundInterictal high frequency oscillations (HFO) are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant HFO in intracranial EEG (iEEG) from MNI Montreal and tested it in iEEG from Zurich. We here validate the algorithm on iEEG recorded in an independent epilepsy center so that HFO analysis was blinded to seizure outcome.MethodsWe selected consecutive patients from Geneva University Hospitals who underwent resective epilepsy surgery with postsurgical follow-up > 12 months. We analyzed long-term iEEG recordings during non-rapid eye movement (NREM) sleep that we segmented into intervals of 5 min. HFOs were defined in the ripple (80-250 Hz) and the fast ripple (FR, 250-500 Hz) frequency band. Contacts with the highest rate of ripples co-occurring with FR (FRandR) designated the HFO area. If the HFO area was not fully resected and the patient suffered from recurrent seizures (ILAE 2-6), this was classified as a true positive (TP) prediction.ResultsWe included iEEG recordings from 16 patients (median age 32 y, range [18-53]) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 mo, range [12-55]). The HFO area had high test-retest reliability across intervals (median dwell time 95%). We excluded two patients with dwell time < 50% from further analysis.The HFO area was fully included in the resected volume in 2/4 patients who achieved postoperative seizure freedom (ILAE 1, specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (ILAE > 1, sensitivity 90%), leading to an accuracy of 79%.ConclusionsWe validated the automated procedure to delineate the clinical relevant HFO area in individual patients of an independently recorded dataset and achieved the same good accuracy as in our previous studies.SignificanceThe reproducibility of our results across datasets is promising for a multicienter study testing the clinical application of HFO detection to guide epilepsy surgery.

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

Scientific Reports ◽

10.1038/s41598-021-85827-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Karla Burelo ◽

Mohammadali Sharifshazileh ◽

Niklaus Krayenbühl ◽

Georgia Ramantani ◽

Giacomo Indiveri ◽

...

Keyword(s):

Neural Network ◽

Real Time ◽

Epilepsy Surgery ◽

High Frequency ◽

Seizure Outcome ◽

Seizure Freedom ◽

Epileptogenic Zone ◽

Spiking Neural Network ◽

High Frequency Oscillations ◽

Neuromorphic Hardware

AbstractTo 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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Blinded study: prospectively defined high frequency oscillations predict seizure outcome in individual patients

Brain Communications ◽

10.1093/braincomms/fcab209 ◽

2021 ◽

Author(s):

Vasileios Dimakopoulos ◽

Pierre Mégevand ◽

Ece Boran ◽

Shahan Momjian ◽

Margitta Seeck ◽

...

Keyword(s):

Epilepsy Surgery ◽

High Frequency ◽

Dwell Time ◽

Seizure Outcome ◽

Seizure Freedom ◽

Intracranial Eeg ◽

Retest Reliability ◽

High Frequency Oscillations ◽

Test Retest Reliability

Abstract Interictal high frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with postsurgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 minutes. High frequency oscillations were defined in the ripple (80-250 Hz) and the fast ripple (250-500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test-retest reliability of the high frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18-53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12-55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test-retest reliability across intervals (median dwell time 95%). In two patients, the test-retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved postoperative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high frequency oscillations to guide epilepsy surgery.

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The resolution revolution: Comparing spikes and high frequency oscillations in high-density and standard intra-operative electrocorticography of the same patient

Clinical Neurophysiology ◽

10.1016/j.clinph.2020.02.006 ◽

2020 ◽

Vol 131 (5) ◽

pp. 1040-1043

Author(s):

Willemiek J.E.M. Zweiphenning ◽

Eric van Diessen ◽

Erik J. Aarnoutse ◽

Frans S.S. Leijten ◽

Peter C. van Rijen ◽

...

Keyword(s):

High Frequency ◽

High Density ◽

High Frequency Oscillations

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Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome

Clinical Neurophysiology ◽

10.1016/j.clinph.2016.06.009 ◽

2016 ◽

Vol 127 (9) ◽

pp. 3066-3074 ◽

Cited By ~ 41

Author(s):

Tommaso Fedele ◽

Maryse van ’t Klooster ◽

Sergey Burnos ◽

Willemiek Zweiphenning ◽

Nicole van Klink ◽

...

Keyword(s):

Epilepsy Surgery ◽

High Frequency ◽

Automatic Detection ◽

Seizure Outcome ◽

High Frequency Oscillations

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Simultaneously recorded intracranial and scalp high frequency oscillations help identify patients with poor postsurgical seizure outcome

Clinical Neurophysiology ◽

10.1016/j.clinph.2018.10.016 ◽

2019 ◽

Vol 130 (1) ◽

pp. 128-137 ◽

Cited By ~ 6

Author(s):

N. Kuhnke ◽

C. Klus ◽

M. Dümpelmann ◽

A. Schulze-Bonhage ◽

J. Jacobs

Keyword(s):

High Frequency ◽

Seizure Outcome ◽

High Frequency Oscillations

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

10.21203/rs.3.rs-83699/v1 ◽

2020 ◽

Author(s):

Mohammadali Sharifhazileh ◽

Karla Burelo ◽

Johannes Sarnthein ◽

Giacomo Indiveri

Keyword(s):

Neural Network ◽

Real Time ◽

Epilepsy Surgery ◽

High Frequency ◽

Neural Recording ◽

Clinical Applications ◽

Seizure Outcome ◽

Intracranial Eeg ◽

High Frequency Oscillations ◽

Neuromorphic System

Abstract 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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