Detection of High-Frequency Oscillations by Hybrid Depth Electrodes in Standard Clinical Intracranial EEG Recordings

Depth Electrodes ◽

Seizure Onset Zone ◽

Potential Biomarker ◽

Eeg Recordings ◽

Intracranial Electroencephalography ◽

Intracranial Recordings

Intracranial electroencephalography (iEEG) is used to localize the focus of seizures and determine vital adjacent cortex before epilepsy surgery. The two most commonly used electrode types are subdural and depth electrodes. Foramen ovale electrodes are less often used. Combinations of electrode types are possible. The choice depends on the presumed focus site. Careful planning is needed before implantation, taking into account the results of noninvasive studies. While subdural recordings allow better mapping of functional cortex, depth electrodes can reach deep structures. There are no guidelines on how to read ictal intracranial EEG recordings, but a focal onset (<5 contacts) and a high-frequency onset herald a good prognosis. High-frequency oscillations have been described as a potential biomarker of the seizure onset zone. Intracranial recordings provide a focal but magnified view of the brain, which is also exemplified by the use of microelectrodes, which allow the recording of single-unit or multi-unit activity.

Faculty Opinions recommendation of Value of electrical stimulation and high frequency oscillations (80-500 Hz) in identifying epileptogenic areas during intracranial EEG recordings.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.3024973.2703077 ◽

2010 ◽

Author(s):

Roderick Duncan

Keyword(s):

Electrical Stimulation ◽

High Frequency ◽

Intracranial Eeg ◽

Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings

Nature Communications ◽

10.1038/s41467-018-04549-2 ◽

2018 ◽

Vol 9 (1) ◽

Cited By ~ 42

Author(s):

Stephen V. Gliske ◽

Zachary T. Irwin ◽

Cynthia Chestek ◽

Garnett L. Hegeman ◽

Benjamin Brinkmann ◽

...

Keyword(s):

High Frequency ◽

Intracranial Eeg ◽

Value of electrical stimulation and high frequency oscillations (80-500 Hz) in identifying epileptogenic areas during intracranial EEG recordings

Epilepsia ◽

10.1111/j.1528-1167.2009.02389.x ◽

2010 ◽

Vol 51 (4) ◽

pp. 573-582 ◽

Cited By ~ 33

Author(s):

Julia Jacobs ◽

Maeike Zijlmans ◽

Rina Zelmann ◽

André Olivier ◽

Jeffery Hall ◽

...

Keyword(s):

Electrical Stimulation ◽

High Frequency ◽

Intracranial Eeg ◽

Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings

Clinical Neurophysiology ◽

10.1016/j.clinph.2006.12.019 ◽

2007 ◽

Vol 118 (5) ◽

pp. 1134-1143 ◽

Cited By ~ 158

Author(s):

Andrew B. Gardner ◽

Greg A. Worrell ◽

Eric Marsh ◽

Dennis Dlugos ◽

Brian Litt

Keyword(s):

High Frequency ◽

Automated Detection ◽

Intracranial Eeg ◽

A Neuromorphic Spiking Neural Network Detects Epileptic High Frequency Oscillations in the Scalp EEG

10.21203/rs.3.rs-1061301/v1 ◽

2021 ◽

Author(s):

Karla Burelo ◽

Georgia Ramantani ◽

Giacomo Indiveri ◽

Johannes Sarnthein

Keyword(s):

Low Power ◽

Seizure Frequency ◽

High Frequency ◽

Therapy Monitoring ◽

Non Invasive ◽

Scalp Eeg ◽

Eeg Recordings ◽

Active Epilepsy ◽

Wearable Electronic Devices

Abstract Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware. Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

ANALYSIS OF HIGH-RESOLUTION MICROELECTRODE EEG RECORDINGS IN AN ANIMAL MODEL OF SPONTANEOUS LIMBIC SEIZURES

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127409023226 ◽

2009 ◽

Vol 19 (02) ◽

pp. 605-617 ◽

Cited By ~ 6

Author(s):

C. KOMALAPRIYA ◽

M. C. ROMANO ◽

M. THIEL ◽

U. SCHWARZ ◽

J. KURTHS ◽

...

Keyword(s):

Animal Model ◽

High Resolution ◽

High Frequency ◽

Recurrence Time ◽

Very High Frequency ◽

Limbic Seizures ◽

Quantification Analysis ◽

We perform a systematic data analysis on high resolution (0.5–12 kHz) multiarray microelectrode recordings from an animal model of spontaneous limbic epilepsy, to investigate the role of high frequency oscillations and the occurrence of early precursors for seizures. Results of spectral analysis confirm the importance of very high frequency oscillations (even greater than 600 Hz) in normal (healthy) and abnormal (epileptic) hippocampus. Furthermore, we show that the measures of Recurrence Quantification Analysis (RQA) and Recurrence Time Statistics (RTS) are successful in indicating, rather uniquely, the onset of ictal state and the occurrence of some warnings/precursors during the pre-ictal state, in contrast to the linear measures investigated.

P361 Test-Retest reliability of the spatial distribution of high frequency oscillations (HFO) in intracranial EEG

Clinical Neurophysiology ◽

10.1016/j.clinph.2017.07.369 ◽

2017 ◽

Vol 128 (9) ◽

pp. e295 ◽

Cited By ~ 1

Author(s):

Ece Boran ◽

Sergey Burnos ◽

Tommaso Fedele ◽

Niklaus Krayenbühl ◽

Peter Hilfiker ◽

...

Keyword(s):

Spatial Distribution ◽

High Frequency ◽

Intracranial Eeg ◽

Retest Reliability ◽

Test Retest Reliability

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

10.1101/2020.12.24.20248799 ◽

2020 ◽

Author(s):

V Dimakopoulos ◽

P Mégevand ◽

E Boran ◽

S Momjian ◽

M Seeck ◽

...

Keyword(s):

Epilepsy Surgery ◽

High Frequency ◽

Dwell Time ◽

Nrem Sleep ◽

Seizure Outcome ◽

Seizure Freedom ◽

University Hospitals ◽

Depth Electrodes

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.