Resection of high frequency oscillations predicts seizure outcome in the individual patient

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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FV 24 High density ECoG improves detection of high frequency oscillations that predict seizure outcome

Clinical Neurophysiology ◽

10.1016/j.clinph.2019.04.633 ◽

2019 ◽

Vol 130 (8) ◽

pp. e133

Author(s):

E. Boran ◽

G. Ramantani ◽

N. Krayenbühl ◽

T. Fedele ◽

J. Sarnthein

Keyword(s):

High Frequency ◽

High Density ◽

Seizure Outcome ◽

High Frequency Oscillations

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P57 High Frequency Oscillations in scalp EEG to monitor therapy in the individual patient

Clinical Neurophysiology ◽

10.1016/j.clinph.2019.04.701 ◽

2019 ◽

Vol 130 (8) ◽

pp. e169

Author(s):

E. Boran ◽

G. Ramantani ◽

N. Krayenbühl ◽

J. Sarnthein ◽

T. Fedele

Keyword(s):

High Frequency ◽

Scalp Eeg ◽

High Frequency Oscillations ◽

The Individual

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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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