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

2017 ◽  
Vol 128 (9) ◽  
pp. e295 ◽  
Author(s):  
Ece Boran ◽  
Sergey Burnos ◽  
Tommaso Fedele ◽  
Niklaus Krayenbühl ◽  
Peter Hilfiker ◽  
...  
Author(s):  
Vasileios Dimakopoulos ◽  
Pierre Mégevand ◽  
Ece Boran ◽  
Shahan Momjian ◽  
Margitta Seeck ◽  
...  

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.


2014 ◽  
Vol 5 ◽  
Author(s):  
Efstathios D. Kondylis ◽  
Thomas A. Wozny ◽  
Witold J. Lipski ◽  
Alexandra Popescu ◽  
Vincent J. DeStefino ◽  
...  

2022 ◽  
Vol 73 ◽  
pp. 103418
Author(s):  
Fatma Krikid ◽  
Ahmad Karfoul ◽  
Sahbi Chaibi ◽  
Amar Kachenoura ◽  
Anca Nica ◽  
...  

2020 ◽  
Author(s):  
Zhuying Chen ◽  
David B. Grayden ◽  
Anthony N. Burkitt ◽  
Udaya Seneviratne ◽  
Wendyl J. D’Souza ◽  
...  

AbstractObjectiveTo assess the variability in the rates and locations of high-frequency activity (HFA) and epileptiform spikes after electrode implantation, and to examine the long-term patterns of HFA using ambulatory intracranial EEG (iEEG) recordings.MethodsContinuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy were used in this study. HFA was defined as high-frequency events with amplitudes clearly larger than the background, which was automatically detected using a custom algorithm. High-frequency oscillations (HFOs) were also visually annotated by three neurologists in randomly sampled segments of the total data. The automatically detected HFA was compared with the visually marked HFOs. The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases.ResultsHFA was a more general event that encompassed HFOs manually annotated by different reviewers. HFA and spike rates had high amounts of intra- and inter-patient variability. The rates and locations of HFA and spikes took up to weeks to stabilize after electrode implantation in some patients. Both HFA and spike rates showed strong circadian rhythms in all patients and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes.ConclusionsAnalysis of HFA and epileptiform spikes should account for post-implantation variability. Like seizures, HFA and epileptiform spikes show circadian rhythms. However, the circadian profiles can vary spatially within patients and their correlations to seizures are patient-specific.


2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Stephen V. Gliske ◽  
Zachary T. Irwin ◽  
Cynthia Chestek ◽  
Garnett L. Hegeman ◽  
Benjamin Brinkmann ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mohammadali Sharifshazileh ◽  
Karla Burelo ◽  
Johannes Sarnthein ◽  
Giacomo Indiveri

AbstractThe analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.


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