High-frequency Oscillations and the Seizure Onset Zones in Neocortical Epilepsy

Abstract High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80–500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers. 5721572971001 awx374media1 5721572971001

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Using ictal high-frequency oscillations (80–500Hz) to localize seizure onset zones in childhood absence epilepsy: A MEG study

Neuroscience Letters ◽

10.1016/j.neulet.2014.02.038 ◽

2014 ◽

Vol 566 ◽

pp. 21-26 ◽

Cited By ~ 31

Author(s):

Ailiang Miao ◽

Jing Xiang ◽

Lu Tang ◽

Huaiting Ge ◽

Hongxing Liu ◽

...

Keyword(s):

High Frequency ◽

Absence Epilepsy ◽

Childhood Absence Epilepsy ◽

Seizure Onset ◽

High Frequency Oscillations ◽

Seizure Onset Zones

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Fast, Accurate Localization of Epileptic Seizure Onset Zones Based on Detection of High-Frequency Oscillations Using Improved Wavelet Transform and Matching Pursuit Methods

Neural Computation ◽

10.1162/neco_a_00899 ◽

2017 ◽

Vol 29 (1) ◽

pp. 194-219 ◽

Cited By ~ 9

Author(s):

Min Wu ◽

Ting Wan ◽

Xiongbo Wan ◽

Yuxiao Du ◽

Jinhua She

Keyword(s):

Wavelet Transform ◽

Epileptic Seizure ◽

High Frequency ◽

Matching Pursuit ◽

Frequency Resolution ◽

Adaptive Genetic Algorithm ◽

Seizure Onset ◽

High Frequency Oscillations ◽

Mp Method ◽

Seizure Onset Zones

This letter describes the improvement of two methods of detecting high-frequency oscillations (HFOs) and their use to localize epileptic seizure onset zones (SOZs). The wavelet transform (WT) method was improved by combining the complex Morlet WT with Shannon entropy to enhance the temporal-frequency resolution during HFO detection. And the matching pursuit (MP) method was improved by combining it with an adaptive genetic algorithm to improve the speed and accuracy of the calculations for HFO detection. The HFOs detected by these two methods were used to localize SOZs in five patients. A comparison shows that the improved WT method provides high specificity and quick localization and that the improved MP method provides high sensitivity.

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A New Unsupervised Detector of High-Frequency Oscillations in Accurate Localization of Epileptic Seizure Onset Zones

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2018.2877820 ◽

2018 ◽

Vol 26 (12) ◽

pp. 2280-2289 ◽

Cited By ~ 9

Author(s):

Min Wu ◽

Ting Wan ◽

Min Ding ◽

Xiongbo Wan ◽

Yuxiao Du ◽

...

Keyword(s):

Epileptic Seizure ◽

High Frequency ◽

Seizure Onset ◽

Accurate Localization ◽

High Frequency Oscillations ◽

Seizure Onset Zones

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Interictal high frequency background activity as a biomarker of epileptogenic tissue

Brain Communications ◽

10.1093/braincomms/fcab188 ◽

2021 ◽

Author(s):

Truman Stovall ◽

Brian Hunt ◽

Simon Glynn ◽

William C Stacey ◽

Stephen V Gliske

Keyword(s):

Logistic Regression ◽

Confidence Interval ◽

High Frequency ◽

High Frequency Oscillation ◽

Epileptogenic Zone ◽

Odds Ratios ◽

Seizure Onset ◽

High Frequency Oscillations ◽

Seizure Onset Zone ◽

Background Data

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.

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Patient-specific optimization of automated detection improves seizure onset zone localization based on high frequency oscillations

10.1101/2020.09.14.297309 ◽

2020 ◽

Author(s):

Casey L. Trevino ◽

Jack J. Lin ◽

Indranil Sen-Gupta ◽

Beth A. Lopour

Keyword(s):

High Frequency ◽

Classification Accuracy ◽

Patient Specific ◽

Seizure Onset ◽

Data Set ◽

Localization Accuracy ◽

High Frequency Oscillations ◽

Seizure Onset Zone ◽

Wide Range ◽

The Impact

AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptogenicity, and automated algorithms are critical tools for their detection. However, previously validated algorithms often exhibit decreased HFO detection accuracy when applied to a new data set, if the parameters are not optimized. This likely contributes to decreased seizure localization accuracy, but this has never been tested. Therefore, we evaluated the impact of parameter selection on seizure onset zone (SOZ) localization using automatically detected HFOs. We detected HFOs in intracranial EEG from twenty medically refractory epilepsy patients with seizure free surgical outcomes using an automated algorithm. For each patient, we assessed classification accuracy of channels inside/outside the SOZ using a wide range of detection parameters and identified the parameters associated with maximum classification accuracy. We found that only three out of twenty patients achieved maximal localization accuracy using conventional HFO detection parameters, and optimal parameter ranges varied significantly across patients. The parameters for amplitude threshold and root-mean-square window had the greatest impact on SOZ localization accuracy; minimum event duration and rejection of false positive events did not significantly affect the results. Using individualized optimal parameters led to substantial improvements in localization accuracy, particularly in reducing false positives from non-SOZ channels. We conclude that optimal HFO detection parameters are patient-specific, often differ from conventional parameters, and have a significant impact on SOZ localization. This suggests that individual variability should be considered when implementing automatic HFO detection as a tool for surgical planning.

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