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

2020 ◽  
Vol 20 (6) ◽  
pp. 338-347
Author(s):  
Julia Jacobs ◽  
Maeike Zijlmans

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.

Neurology ◽  
2009 ◽  
Vol 72 (11) ◽  
pp. 979-986 ◽  
Author(s):  
M. Zijlmans ◽  
J. Jacobs ◽  
R. Zelmann ◽  
F. Dubeau ◽  
J. Gotman

2018 ◽  
Vol 20 (2) ◽  
pp. 76-80
Author(s):  
N B Arkhipova ◽  
A Yu Ulitin ◽  
M V Alexandrov

High-frequency bioelectrical activity in pharmacoresistant epilepsy is analyzed. It has been established that pathologic high-frequency oscillations are a potential marker of the epileptogenic zone. We propose a classification of pathological high-frequency oscillations: 1) continuous high-frequency oscillations; 2) modulated pathological high-frequency oscillations, associated with slow waves; 3) modulated pathological high-frequency oscillations, associated with spikes. An example illustrating the application of the analysis of high-frequency bioelectrical activity for the localization of the epileptogenic zone in the widespread irritative zone is given. Variants of interrelation of the regions generating pathological high-frequency activity and epileptic activity in the range up to 70 Hz are demonstrated. Recording of epileptic activity in the frequency range up to 70 Hz is not an exclusive criterion of the epileptogenesis. Recording of modulated pathological high- frequency oscillations associated with spikes allows the differentiation of two spike types. We can assume that the mechanism for generating spikes containing high-frequency component differs from the one for plain spikes. The generators of the pathological high-frequency oscillations are characterized by a smaller size, which should allow more precise localization of the focus of pathological activity. In some cases, the analysis of the high-frequency component of the electrocorticogram makes it possible to differentiate the secondary irritative zone. It has been demonstrated that in patients with extratemporal, especially frontal, epileptogenic zone localization pathological high-frequency oscillations provide additional information about the location of the generator of abnormal activity.


Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 52
Author(s):  
Oleksandr Makeyev ◽  
Frederick Lee ◽  
Mark Musngi

Epilepsy affects approximately 67 million people worldwide with up to 75% from developing countries. Diagnosing epilepsy using electroencephalogram (EEG) is complicated due to its poor signal-to-noise ratio, high sensitivity to various forms of artifacts, and low spatial resolution. Laplacian EEG signal via novel and noninvasive tripolar concentric ring electrodes (tEEG) is superior to EEG via conventional disc electrodes due to its unique capabilities, which allow automatic attenuation of common movement and muscle artifacts. In this work, we apply exponentially embedded family (EEF) to show feasibility of automatic detection of gamma band high-frequency oscillations (HFOs) in tEEG data from two human patients with epilepsy as a step toward the ultimate goal of using the automatically detected HFOs as auxiliary features for seizure onset detection to improve diagnostic yield of tEEG for epilepsy. Obtained preliminary results suggest the potential of the approach and feasibility of detecting HFOs in tEEG data using the EEF based detector with high accuracy. Further investigation on a larger dataset is needed for a conclusive proof.


Author(s):  
Truman Stovall ◽  
Brian Hunt ◽  
Simon Glynn ◽  
William C Stacey ◽  
Stephen V Gliske

Abstract High Frequency Oscillations are very brief events that are a well-established biomarker of the epileptogenic zone, but are rare and comprise only a tiny fraction of the total recorded EEG. We hypothesize that the interictal high frequency “background” data, which has received little attention but represents the majority of the EEG record, also may contain additional, novel information for identifying the epileptogenic zone. We analyzed intracranial EEG (30–500 Hz frequency range) acquired from 24 patients who underwent resective surgery. We computed 38 quantitative features based on all usable, interictal data (63–307 hours per subject), excluding all detected high frequency oscillations. We assessed association between each feature and the seizure onset zone and resected volume using logistic regression. A pathology score per channel was also created via principle component analysis and logistic regression, using hold-out-one-patient cross validation to avoid in-sample training. Association of the pathology score with the seizure onset zone and resected volume was quantified using an asymmetry measure. Many features were associated with the seizure onset zone: 23/38 features had odds ratios >1.3 or < 0.7 and 17/38 had odds ratios different than zero with high significance (p < 0.001/39, logistic regression with Bonferroni Correction). The pathology score, the rate of high frequency oscillations, and their channel-wise product were each strongly associated with the seizure onset zone (median asymmetry > =0.44, good surgery outcome patients; median asymmetry > =0.40, patients with other outcomes; 95% confidence interval > 0.27 in both cases). The pathology score and the channel-wise product also had higher asymmetry with respect to the seizure onset zone than the high frequency oscillation rate alone (median difference in asymmetry > =0.18, 95% confidence interval >0.05). These results support that the high frequency background data contains useful information for determining the epileptogenic zone, distinct and complementary to information from detected high frequency oscillations. The concordance between the high frequency activity pathology score and the rate of high frequency oscillations appears to be a better biomarker of epileptic tissue than either measure alone.


2019 ◽  
Vol 11 (514) ◽  
pp. eaax7830 ◽  
Author(s):  
Su Liu ◽  
Josef Parvizi

Epileptic brain tissue is often considered physiologically dysfunctional, and the optimal treatment of many patients with uncontrollable seizures involves surgical removal of the epileptic tissue. However, it is unclear to what extent the epileptic tissue is capable of generating physiological responses to cognitive stimuli and how cognitive deficits ensuing surgical resections can be determined using state-of-the-art computational methods. To address these unknowns, we recruited six patients with nonlesional epilepsies and identified the epileptic focus in each patient with intracranial electrophysiological monitoring. We measured spontaneous epileptic activity in the form of high-frequency oscillations (HFOs), recorded stimulus-locked physiological responses in the form of physiological high-frequency broadband activity, and explored the interaction of the two as well as their behavioral correlates. Across all patients, we found abundant normal physiological responses to relevant cognitive stimuli in the epileptic sites. However, these physiological responses were more likely to be “seized” (delayed or missed) when spontaneous HFOs occurred about 850 to 1050 ms before, until about 150 to 250 ms after, the onset of relevant cognitive stimuli. Furthermore, spontaneous HFOs in medial temporal lobe affected the subjects’ memory performance. Our findings suggest that nonlesional epileptic sites are capable of generating normal physiological responses and highlight a compelling mechanism for cognitive deficits in these patients. The results also offer clinicians a quantitative tool to differentiate pathological and physiological high-frequency activities in epileptic sites and to indirectly assess their possible cognitive reserve function and approximate the risk of resective surgery.


2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Peter Höller ◽  
Eugen Trinka ◽  
Yvonne Höller

High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was taken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this review, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the available algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the low signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the high-frequency range.


Author(s):  
Walter G. Besio ◽  
Iris E. Martinez-Juarez ◽  
Oleksandr Makeyev ◽  
John N. Gaitanis ◽  
Andrew S. Blum ◽  
...  

2016 ◽  
Vol 127 (4) ◽  
pp. 2140-2148 ◽  
Author(s):  
Sergey Burnos ◽  
Birgit Frauscher ◽  
Rina Zelmann ◽  
Claire Haegelen ◽  
Johannes Sarnthein ◽  
...  

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