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

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.

Automatic Detection and Classification of High-Frequency Oscillations in Depth-EEG Signals

2017 ◽  
Vol 64 (9) ◽  
pp. 2230-2240 ◽  
Author(s):  
Nisrine Jrad ◽  
Amar Kachenoura ◽  
Isabelle Merlet ◽  
Fabrice Bartolomei ◽  
Anca Nica ◽  
...  
Keyword(s):  
High Frequency ◽  
Automatic Detection ◽  
Eeg Signals ◽  
High Frequency Oscillations

scholarly journals Automatic detection of high frequency oscillations during epilepsy surgery predicts seizure outcome

2016 ◽  
Vol 127 (9) ◽  
pp. 3066-3074 ◽  
Author(s):  
Tommaso Fedele ◽  
Maryse van ’t Klooster ◽  
Sergey Burnos ◽  
Willemiek Zweiphenning ◽  
Nicole van Klink ◽  
...  
Keyword(s):  
Epilepsy Surgery ◽  
High Frequency ◽  
Automatic Detection ◽  
Seizure Outcome ◽  
High Frequency Oscillations

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.

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