Automatic Localization of Seizure Onset Zone from High-frequency SEEG Signals: A Preliminary Study

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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Decision Support System for Seizure Onset Zone Localization Based on Channel Ranking and High-Frequency EEG Activity

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2018.2867875 ◽

2019 ◽

Vol 23 (4) ◽

pp. 1535-1545 ◽

Cited By ~ 4

Author(s):

Stefan L. Sumsky ◽

Sabato Santaniello

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

High Frequency ◽

Seizure Onset ◽

Eeg Activity ◽

Seizure Onset Zone

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Special recording techniques for detection of the seizure onset zone: DC shifts and high-frequency discharges

Handbook of Clinical Neurophysiology - Presurgical Assessment of the Epilepsies with Clinical Neurophysiology and Functional Imaging ◽

10.1016/s1567-4231(03)03010-7 ◽

2003 ◽

pp. 135-145 ◽

Cited By ~ 1

Author(s):

Akio Ikeda ◽

Hiroshi Shibasaki

Keyword(s):

High Frequency ◽

Seizure Onset ◽

Seizure Onset Zone ◽

Recording Techniques

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Spatial variation in high-frequency oscillation rates and amplitudes in intracranial EEG

Neurology ◽

10.1212/wnl.0000000000004998 ◽

2018 ◽

Vol 90 (8) ◽

pp. e639-e646 ◽

Cited By ~ 23

Author(s):

Hari Guragain ◽

Jan Cimbalnik ◽

Matt Stead ◽

David M. Groppe ◽

Brent M. Berry ◽

...

Keyword(s):

High Frequency ◽

Anatomic Variation ◽

Brain Regions ◽

Brain Atlas ◽

Reproducible Research ◽

High Frequency Oscillation ◽

Seizure Onset ◽

Frequency Oscillation ◽

Intracranial Eeg ◽

Seizure Onset Zone

ObjectiveTo assess the variation in baseline and seizure onset zone interictal high-frequency oscillation (HFO) rates and amplitudes across different anatomic brain regions in a large cohort of patients.MethodsSeventy patients who had wide-bandwidth (5 kHz) intracranial EEG (iEEG) recordings during surgical evaluation for drug-resistant epilepsy between 2005 and 2014 who had high-resolution MRI and CT imaging were identified. Discrete HFOs were identified in 2-hour segments of high-quality interictal iEEG data with an automated detector. Electrode locations were determined by coregistering the patient's preoperative MRI with an X-ray CT scan acquired immediately after electrode implantation and correcting electrode locations for postimplant brain shift. The anatomic locations of electrodes were determined using the Desikan-Killiany brain atlas via FreeSurfer. HFO rates and mean amplitudes were measured in seizure onset zone (SOZ) and non-SOZ electrodes, as determined by the clinical iEEG seizure recordings. To promote reproducible research, imaging and iEEG data are made freely available (msel.mayo.edu).ResultsBaseline (non-SOZ) HFO rates and amplitudes vary significantly in different brain structures, and between homologous structures in left and right hemispheres. While HFO rates and amplitudes were significantly higher in SOZ than non-SOZ electrodes when analyzed regardless of contact location, SOZ and non-SOZ HFO rates and amplitudes were not separable in some lobes and structures (e.g., frontal and temporal neocortex).ConclusionsThe anatomic variation in SOZ and non-SOZ HFO rates and amplitudes suggests the need to assess interictal HFO activity relative to anatomically accurate normative standards when using HFOs for presurgical planning.

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