scholarly journals Feasibility of Automatic Detection of High-Frequency Oscillations in Human Tripolar Laplacian Electroencephalogram Using Exponentially Embedded Family

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 52
Author(s):  
Oleksandr Makeyev ◽  
Frederick Lee ◽  
Mark Musngi
Keyword(s):  
High Frequency ◽  
Diagnostic Yield ◽  
Signal To Noise Ratio ◽  
Automatic Detection ◽  
High Sensitivity ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Patients With Epilepsy ◽  
Disc Electrodes ◽  
Electroencephalogram Eeg

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.

scholarly journals High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Peter Höller ◽  
Eugen Trinka ◽  
Yvonne Höller
Keyword(s):  
High Frequency ◽  
Visual Detection ◽  
Signal To Noise Ratio ◽  
Physiological Activity ◽  
Automated Detection ◽  
Detection Algorithms ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg

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.

High-Frequency Oscillations (HFO)

2018 ◽  
pp. 131-148
Author(s):  
Julia Jacobs
Keyword(s):  
High Frequency ◽  
Automatic Detection ◽  
Brain Regions ◽  
Seizure Freedom ◽  
Seizure Onset ◽  
Clinical Value ◽  
Brain Areas ◽  
High Frequency Oscillations ◽  
Epileptic Spikes ◽  
Presurgical Assessment

High-frequency oscillations (HFO) are new EEG biomarkers for epileptic tissue. These oscillations range in frequencies from 80 to 500 Hz and can be recorded with standard intracranial macroelectrodes. During the presurgical assessment of patients with refractory epilepsy, HFO have been found to occur mainly over seizure onset areas. HFO might co-occur with epileptic spikes, but are more specific to epileptic tissue than epileptic spikes. Several retrospective studies showed a correlation between the removal of brain areas generating HFO and postsurgical seizure freedom. In addition to demonstrating the clinical value of HFO analysis, this chapter provides a detailed introduction to the techniques for analysing HFO, including recording techniques and visual and automatic detection tools, and to interpretation of the results. It also reviews methodological challenges such as the occurrence of physiological HFO and the variability of HFO rates between patients and brain regions.

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
Keyword(s):  
Disease Activity ◽  
High Frequency ◽  
Spatial Location ◽  
Epileptic Activity ◽  
Epileptogenic Zone ◽  
High Frequency Oscillations ◽  
Patients With Epilepsy ◽  
Electroencephalogram Eeg

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.

scholarly journals Interictal high frequency background activity as a biomarker of epileptogenic tissue

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.

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

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.

scholarly journals DC shifts, high frequency oscillations, ripples and fast ripples in relation to the seizure onset zone

Seizure ◽  
2020 ◽  
Vol 77 ◽  
pp. 52-58 ◽  
Author(s):  
Somin Lee ◽  
Naoum P. Issa ◽  
Sandra Rose ◽  
James X. Tao ◽  
Peter C. Warnke ◽  
...  
Keyword(s):  
High Frequency ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone

Automatic detection of high-frequency-oscillations and their sub-groups co-occurring with interictal-epileptic-spikes

2020 ◽  
Vol 17 (1) ◽  
pp. 016030 ◽  
Author(s):  
Daniel Lachner-Piza ◽  
Julia Jacobs ◽  
Jonas C Bruder ◽  
Andreas Schulze-Bonhage ◽  
Thomas Stieglitz ◽  
...  
Keyword(s):  
High Frequency ◽  
Automatic Detection ◽  
High Frequency Oscillations ◽  
Epileptic Spikes

scholarly journals Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy

Brain ◽  
2018 ◽  
Vol 141 (3) ◽  
pp. 713-730 ◽  
Author(s):  
Su Liu ◽  
Candan Gurses ◽  
Zhiyi Sha ◽  
Michael M Quach ◽  
Altay Sencer ◽  
...  
Keyword(s):  
High Frequency ◽  
Focal Epilepsy ◽  
Machine Learning Techniques ◽  
Seizure Onset ◽  
Functional Regions ◽  
Clinical Biomarkers ◽  
High Frequency Oscillations ◽  
Potential Association ◽  
Waveform Similarity ◽  
Seizure Onset Zones

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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