scholarly journals Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1415
Author(s):  
Most. Sheuli Akter ◽  
Md. Rabiul Islam ◽  
Toshihisa Tanaka ◽  
Yasushi Iimura ◽  
Takumi Mitsuhashi ◽  
...  
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Focal Epilepsy ◽  
Epileptic Seizures ◽  
Gradient Boosting ◽  
Statistical Features ◽  
Seizure Onset ◽  
Seizure Onset Zone ◽  
Frequency Components ◽  
Final Score

The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods.

scholarly journals Localisation in focal epilepsy: a practical guide

2021 ◽  
pp. practneurol-2019-002341
Author(s):  
Fahmida A Chowdhury ◽  
Rui Silva ◽  
Benjamin Whatley ◽  
Matthew C Walker
Keyword(s):  
Current Literature ◽  
Focal Epilepsy ◽  
Correct Diagnosis ◽  
Epileptic Seizures ◽  
Seizure Onset ◽  
Practical Guide ◽  
Seizure Onset Zone ◽  
Insight Into

The semiology of epileptic seizures reflects activation, or dysfunction, of areas of brain (often termed the symptomatogenic zone) as a seizure begins and evolves. Specific semiologies in focal epilepsies provide an insight into the location of the seizure onset zone, which is particularly important for presurgical epilepsy assessment. The correct diagnosis of paroxysmal events also depends on the clinician being familiar with the spectrum of semiologies. Here, we summarise the current literature on localisation in focal epilepsies using illustrative cases and discussing possible pitfalls in localisation.

scholarly journals Phase-amplitude coupling between infraslow and high-frequency activities well discriminates between the preictal and interictal states

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroaki Hashimoto ◽  
Hui Ming Khoo ◽  
Takufumi Yanagisawa ◽  
Naoki Tani ◽  
Satoru Oshino ◽  
...  
Keyword(s):  
High Frequency ◽  
Focal Epilepsy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Discrimination Performance ◽  
Epileptic Seizures ◽  
Electrode Placement ◽  
Seizure Onset ◽  
Phase Amplitude ◽  
Preictal State

AbstractInfraslow activity (ISA) and high-frequency activity (HFA) are key biomarkers for studying epileptic seizures. We aimed to elucidate the relationship between ISA and HFA around seizure onset. We enrolled seven patients with drug-resistant focal epilepsy who underwent intracranial electrode placement. We comparatively analyzed the ISA, HFA, and ISA-HFA phase-amplitude coupling (PAC) in the seizure onset zone (SOZ) or non-SOZ (nSOZ) in the interictal, preictal, and ictal states. We recorded 15 seizures. HFA and ISA were larger in the ictal states than in the interictal or preictal state. During seizures, the HFA and ISA of the SOZ were larger and occurred earlier than those of nSOZ. In the preictal state, the ISA-HFA PAC of the SOZ was larger than that of the interictal state, and it began increasing at approximately 87 s before the seizure onset. The receiver-operating characteristic curve revealed that the ISA-HFA PAC of the SOZ showed the highest discrimination performance in the preictal and interictal states, with an area under the curve of 0.926. This study demonstrated the novel insight that ISA-HFA PAC increases before the onset of seizures. Our findings indicate that ISA-HFA PAC could be a useful biomarker for discriminating between the preictal and interictal states.

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

Neurology ◽  
2018 ◽  
Vol 90 (8) ◽  
pp. e639-e646 ◽  
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.

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

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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