scholarly journals Channel-Wise Characterization of High Frequency Oscillations for Automated Identification of the Seizure Onset Zone

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 45531-45543 ◽  
Author(s):  
Dakun Lai ◽  
Xinyue Zhang ◽  
Wenjing Chen ◽  
Heng Zhang ◽  
Tongzhou Kang ◽  
...  
Keyword(s):  
High Frequency ◽  
Automated Identification ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone

scholarly journals Kurtosis-Based Detection of Intracranial High-Frequency Oscillations for the Identification of the Seizure Onset Zone

2018 ◽  
Vol 28 (07) ◽  
pp. 1850001 ◽  
Author(s):  
Lucia Rita Quitadamo ◽  
Roberto Mai ◽  
Francesca Gozzo ◽  
Veronica Pelliccia ◽  
Francesco Cardinale ◽  
...  
Keyword(s):  
High Frequency ◽  
Computational Time ◽  
Seizure Onset ◽  
Time Frequency ◽  
High Frequency Oscillations ◽  
Epileptiform Discharges ◽  
Seizure Onset Zone ◽  
Potential Biomarker ◽  
Eeg Data

Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time–frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80–250 Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%.

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

scholarly journals Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Annika Minthe ◽  
Wibke G Janzarik ◽  
Daniel Lachner-Piza ◽  
Peter Reinacher ◽  
Andreas Schulze-Bonhage ◽  
...  
Keyword(s):  
High Frequency ◽  
Background Activity ◽  
Sleep Stage ◽  
Strong Association ◽  
Brain Regions ◽  
High Frequency Oscillation ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Low Entropy

Abstract High-frequency oscillations are markers of epileptic tissue. Recently, different patterns of EEG background activity were described from which high-frequency oscillations occur: high-frequency oscillations with continuously oscillating background were found to be primarily physiological, those from quiet background were linked to epileptic tissue. It is unclear, whether these interactions remain stable over several days and during different sleep-wake stages. High-frequency oscillation patterns (oscillatory vs. quiet background) were analysed in 23 patients implanted with depth and subdural grid electrodes. Pattern scoring was performed on every channel in 10 s intervals in three separate day- and night-time EEG segments. An entropy value, measuring variability of patterns per channel, was calculated. A low entropy value indicated a stable occurrence of the same pattern in one channel, whereas a high value indicated pattern instability. Differences in pattern distribution and entropy were analysed for 143 280 10 s intervals with allocated patterns from inside and outside the seizure onset zone, different electrode types and brain regions. We found a strong association between high-frequency oscillations out of quiet background activity, and channels of the seizure onset zone (35.2% inside versus 9.7% outside the seizure onset zone, P < 0.001), no association was found for high-frequency oscillations from continuous oscillatory background (P = 0.563). The type of background activity remained stable over the same brain region over several days and was independent of sleep stage and recording technique. Stability of background activity was significantly higher in channels of the seizure onset zone (entropy mean value 0.56 ± 0.39 versus 0.64 ± 0.41; P < 0.001). This was especially true for the presumed epileptic high-frequency oscillations out of quiet background (0.57 ± 0.39 inside versus 0.72 ± 0.37 outside the seizure onset zone; P < 0.001). In contrast, presumed physiological high-frequency oscillations from continuous oscillatory backgrounds were significantly more stable outside the seizure onset zone (0.72 ± 0.45 versus 0.48 ± 0.53; P < 0.001). The overall low entropy values suggest that interactions between high-frequency oscillations and background activity are a stable phenomenon specific to the function of brain regions. High-frequency oscillations occurring from a quiet background are strongly linked to the seizure onset zone whereas high-frequency oscillations from an oscillatory background are not. Pattern stability suggests distinct underlying mechanisms. Analysing short time segments of high-frequency oscillations and background activity could help distinguishing epileptic from physiologically active brain regions.

scholarly journals Amplitude of high frequency oscillations as a biomarker of the seizure onset zone

2020 ◽  
Author(s):  
Krit Charupanit ◽  
Indranil Sen-Gupta ◽  
Jack J Lin ◽  
Beth A Lopour
Keyword(s):  
High Frequency ◽  
Learning Algorithm ◽  
Clinical Implementation ◽  
Seizure Onset ◽  
Receiver Operating Characteristic Curves ◽  
Detection Algorithms ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
And Performance ◽  
Over Time

AbstractObjectiveStudies of high frequency oscillations (HFOs) in epilepsy have primarily tested the HFO rate as a biomarker of the seizure onset zone (SOZ), but the rate varies over time and is not robust for all individual subjects. As an alternative, we tested the performance of HFO amplitude as a potential SOZ biomarker using two automated detection algorithms.MethodHFOs were detected in intracranial electroencephalogram (iEEG) from 11 patients using a machine learning algorithm and a standard amplitude-based algorithm. For each detector, SOZ and non-SOZ channels were classified using the rate and amplitude of high frequency events, and performance was compared using receiver operating characteristic curves.ResultsThe amplitude of detected events was significantly higher in SOZ. Across subjects, amplitude more accurately classified SOZ/non-SOZ than rate (higher values of area under the ROC curve and sensitivity, and lower false positive rates). Moreover, amplitude was more consistent across segments of data, indicated by lower coefficient of variation.ConclusionAs an SOZ biomarker, HFO amplitude offers advantages over HFO rate: it exhibits higher classification accuracy, more consistency over time, and robustness to parameter changes.SignificanceThis biomarker has the potential to increase the generalizability of HFOs and facilitate clinical implementation as a tool for SOZ localization.

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