scholarly journals Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shennan A. Weiss ◽  
Richard J. Staba ◽  
Ashwini Sharan ◽  
Chengyuan Wu ◽  
Daniel Rubinstein ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Repeated Measures ◽  
Roc Curves ◽  
Epileptogenic Zone ◽  
Roc Curve Analysis ◽  
Electrode Implantation ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Generalized Linear Mixed Effects

AbstractTo see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1–2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80–250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250–600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively (p < 0.01). HFO frequency was also affected (p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ (p < 0.01). However, the increases were larger during the sleep recordings (p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates (p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients (p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone.

Automated Seizure Onset Zone Approximation Based on Nonharmonic High-Frequency Oscillations in Human Interictal Intracranial EEGs

2015 ◽  
Vol 25 (05) ◽  
pp. 1550015 ◽  
Author(s):  
Evelien E. Geertsema ◽  
Gerhard H. Visser ◽  
Demetrios N. Velis ◽  
Steven P. Claus ◽  
Maeike Zijlmans ◽  
...  
Keyword(s):  
High Frequency ◽  
Surrogate Marker ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Visual Observation ◽  
Epileptogenic Zone ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Residual Variation ◽  
Seizure Onset Zone

A novel automated algorithm is proposed to approximate the seizure onset zone (SOZ), while providing reproducible output. The SOZ, a surrogate marker for the epileptogenic zone (EZ), was approximated from intracranial electroencephalograms (iEEG) of nine people with temporal lobe epilepsy (TLE), using three methods: (1) Total ripple length (TRL): Manually segmented high-frequency oscillations, (2) Rippleness (R): Area under the curve (AUC) of the autocorrelation functions envelope, and (3) Autoregressive model residual variation (ARR, novel algorithm): Time-variation of residuals from autoregressive models of iEEG windows. TRL, R, and ARR results were compared in terms of separability, using Kolmogorov–Smirnov tests, and performance, using receiver operating characteristic (ROC) curves, to the gold standard for SOZ delineation: visual observation of ictal video-iEEGs. TRL, R, and ARR can distinguish signals from iEEG channels located within the SOZ from those outside it (p < 0.01). The ROC AUC was 0.82 for ARR, while it was 0.79 for TRL, and 0.64 for R. ARR outperforms TRL and R, and may be applied to identify channels in the SOZ automatically in interictal iEEGs of people with TLE. ARR, interpreted as evidence for nonharmonicity of high-frequency EEG components, could provide a new way to delineate the EZ, thus contributing to presurgical workup.

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 &gt;1.3 or &lt; 0.7 and 17/38 had odds ratios different than zero with high significance (p &lt; 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 &gt; =0.44, good surgery outcome patients; median asymmetry &gt; =0.40, patients with other outcomes; 95% confidence interval &gt; 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 &gt; =0.18, 95% confidence interval &gt;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.

Direct Current Shift Recordings

2018 ◽  
pp. 149-160
Author(s):  
Shasha Wu ◽  
Vernon L. Towle ◽  
Jonathan P. Miller ◽  
Hans O. Lüders
Keyword(s):  
Direct Current ◽  
Pyramidal Neurons ◽  
Brain Activity ◽  
Spatial Relationship ◽  
Epileptogenic Zone ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Long Time ◽  
Eeg Recordings ◽  
Current Shift

Direct current (DC) shift, also called infraslow activity (IFA), refers to brain activity slower than 0.5 Hz. DC shifts have been shown to have a close temporal and spatial relationship with the conventional seizure onset zone and a smaller field than the conventional ictal electroencephalogram (EEG); therefore, they may have potential value in identifying the epileptogenic zone. Studies have shown that DC shifts can be reliably recorded intracranially from chronic subdural grids or depth electrodes using AC amplifiers with a long time constant (e.g. 10 s) in a compressed viewing window. Although the mechanism remains under debate, most investigators agree that DC shifts are generated by neuroglial cells or neuroglial cells together with pyramidal neurons. With the widespread use of digital EEG recording, DC shifts, together with high-frequency oscillations should be routinely analysed in invasive EEG recordings to provide complementary information in presurgical evaluation of epilepsy.

Reliability and Concurrent Validity of Shoulder Tissue Irritability Classification

2021 ◽  
Author(s):  
Stephen M Kareha ◽  
Philip W McClure ◽  
Alicia Fernandez-Fernandez
Keyword(s):  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Repeated Measures ◽  
Concurrent Validity ◽  
Reliability And Validity ◽  
Strong Relationship ◽  
Rating System ◽  
Roc Curve Analysis ◽  
Cross Sectional

Abstract Objective Rating tissue irritability has been recommended to aid decision making in several recent clinical practice guidelines. An explicit method for rating tissue irritability was proposed as part of the Staged Algorithm for Rehabilitation Classification: Shoulder Disorders (STAR-Shoulder), but the reliability and validity of this classification are unknown. The purpose of this study was to examine the reliability and concurrent validity of shoulder tissue irritability ratings as part of a system designed to guide appropriate treatment strategy and intensity. Methods A clinical measurement, prospective repeated-measures cross-sectional design was used. The 101 consecutive participants with primary complaints of shoulder pain were assessed by pairs of blinded raters (24 raters in total) and rated for tissue irritability. Patients completed 3 patient-rated outcome (PRO) measures reflecting both pain and disability, and these scores were compared with ratings of tissue irritability. Paired ratings of irritability were analyzed for reliability with prevalence-adjusted, bias-adjusted Kappa for ordinal scales (PABAK-OS). Analysis of variance (ANOVA) was used to compare PRO measures across different levels of irritability. Receiver operating characteristic (ROC) curve analysis was utilized to derive cut-off scores for 3 PRO instruments. Results Interrater reliability was 0.69 (95% CI = 0.59–0.78), with 67% agreement. All PRO measures were significantly different among 3 levels of tissue irritability. Conclusion There appear to be acceptable reliability and a strong relationship between PRO measures and therapist-rated tissue irritability, supporting the use of the STAR-Shoulder irritability rating system. Impact Several clinical practice guidelines have recommended that clinicians rate tissue irritability as part of their examination. This study provides important new information supporting the reliability and validity of the STAR-Shoulder tissue irritability rating system.

scholarly journals Classification of RA Into Active or Inactive With a Modified DAS, for Future Use as a Treat-to-target Tool, With a HandScan Score Replacing Joint Counts

2021 ◽  
Author(s):  
Maxime Verhoeven ◽  
Anton Westgeest ◽  
Janneke Tekstra ◽  
Jacob van Laar ◽  
Floris Lafeber ◽  
...  
Keyword(s):  
Medical Records ◽  
Roc Curves ◽  
Predictor Variables ◽  
Treat To Target ◽  
Reference Standard ◽  
Clinical Classification ◽  
Operating Characteristics ◽  
Clinical Interpretation ◽  
Activity Measures

Abstract ObjectivesTo establish the value of a modified DAS (DAS-OST) without joint counts but with a HandScan score (OST), versus that of DAS28, to classify RA as active versus inactive, with as reference standard the rheumatologist's clinical classification.MethodsRA patients with at least one HandScan and DAS28 measurement performed at the same visit were included. Data was extracted from medical records, as was the clinical interpretation as active or inactive RA by the rheumatologist. Logistic regression analyses were performed to calculate areas under the receiver operating characteristics (AU-ROC) curves. The clinical interpretation was used as reference standard in all analyses, and disease activity measures were used as predictor variables. The performance of predictor variables (AU-ROCs) was compared.ResultsData of 1505 unique RA patients were used for analyses. The highest AU-ROC of 0.88 (95%CI 0.85 – 0.90) was shown for DAS28; AU-ROC of DAS-OST was 0.78 (95%CI 0.75 – 0.81), difference 0.10, p<0.01.ConclusionsCompared to DAS28, DAS-OST classified RA statistically significantly less well as active versus inactive, when using the clinical classification as reference standard. However, a DAS-modification without joint scores might have a place in strategies limiting routine outpatients’ visits to the rheumatologist.

scholarly journals The proportion of seizure onset zone contacts resected is not associated with outcome following SEEG-guided resective epilepsy surgery in children

2021 ◽  
Author(s):  
Mehdi Khan ◽  
Aswin Chari ◽  
Kiran Seunarine ◽  
Christin Eltze ◽  
Friederike Moeller ◽  
...  
Keyword(s):  
Epilepsy Surgery ◽  
Univariate Analysis ◽  
Seizure Outcome ◽  
Epileptogenic Zone ◽  
Imaging Data ◽  
Seizure Onset ◽  
Binary Logistic Regression Model ◽  
Primary Analysis ◽  
Seizure Onset Zone

AbstractPurposeChildren undergoing stereoelectroencephalography (SEEG)-guided epilepsy surgery represent a complex cohort. We aimed to determine whether the proportion of putative seizure onset zone (SOZ) contacts resected associates with seizure outcome in a cohort of children undergoing SEEG-guided resective epilepsy surgery.MethodsPatients who underwent SEEG-guided resective surgery over a six-year period were included. The proportion of SOZ contacts resected was determined by co-registration of pre- and post-operative imaging. Seizure outcomes were classified as seizure free (SF, Engel class I) or not seizure-free (NSF, Engel class II-IV) at last clinical follow-up.ResultsOf 94 patients undergoing SEEG, 29 underwent subsequent focal resection of whom 22 had sufficient imaging data to be included in the primary analysis (median age at surgery of 10 years, range 5-18). Fifteen (68.2%) were SF and 7 (31.8%) NSF at median follow-up of 19.5 months (range 12-46). On univariate analysis, histopathology, was the only significant factor associated with SF (p<0.05). The percentage of defined SOZ contacts resected ranged from 25-100% and was not associated with SF (p=0.89). In a binary logistic regression model, it was highly likely that histology was the only independent predictor of outcome, although the interpretation was limited by pseudo-complete separation of the data.ConclusionHistopathology is a significant predictor of surgical outcomes in children undergoing SEEG-guided resective epilepsy surgery. The percentage of SOZ contacts resected was not associated with SF. Factors such as spatial organisation of the epileptogenic zone, neurophysiological biomarkers and the prospective identification of pathological tissue may therefore play an important role.

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 The Banff Working Group Classification of Definitive Polyomavirus Nephropathy: Morphologic Definitions and Clinical Correlations

2017 ◽  
Vol 29 (2) ◽  
pp. 680-693 ◽  
Author(s):  
Volker Nickeleit ◽  
Harsharan K. Singh ◽  
Parmjeet Randhawa ◽  
Cinthia B. Drachenberg ◽  
Ramneesh Bhatnagar ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Working Group ◽  
Classification Scheme ◽  
Clinical Presentation ◽  
Interstitial Fibrosis ◽  
The United States ◽  
Systematic Analysis ◽  
Definition Of ◽  
Morphologic Classification

Polyomavirus nephropathy (PVN) is a common viral infection of renal allografts, with biopsy-proven incidence of approximately 5%. A generally accepted morphologic classification of definitive PVN that groups histologic changes, reflects clinical presentation, and facilitates comparative outcome analyses is lacking. Here, we report a morphologic classification scheme for definitive PVN from the Banff Working Group on Polyomavirus Nephropathy, comprising nine transplant centers in the United States and Europe. This study represents the largest systematic analysis of definitive PVN undertaken thus far. In a retrospective fashion, clinical data were collected from 192 patients and correlated with morphologic findings from index biopsies at the time of initial PVN diagnosis. Histologic features were centrally scored according to Banff guidelines, including additional semiquantitative histologic assessment of intrarenal polyomavirus replication/load levels. In-depth statistical analyses, including mixed effects repeated measures models and logistic regression, revealed two independent histologic variables to be most significantly associated with clinical presentation: intrarenal polyomavirus load levels and Banff interstitial fibrosis ci scores. These two statistically determined histologic variables formed the basis for the definition of three PVN classes that correlated strongest with three clinical parameters: presentation at time of index biopsy, serum creatinine levels/renal function over 24 months of follow-up, and graft failure. The PVN classes 1–3 as described here can easily be recognized in routine renal biopsy specimens. We recommend using this morphologic PVN classification scheme for diagnostic communication, especially at the time of index diagnosis, and in scientific studies to improve comparative data analysis.

scholarly journals Frameless robot-assisted stereoelectroencephalography in children: technical aspects and comparison with Talairach frame technique

2018 ◽  
Vol 22 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Taylor J. Abel ◽  
René Varela Osorio ◽  
Ricardo Amorim-Leite ◽  
Francois Mathieu ◽  
Philippe Kahane ◽  
...  
Keyword(s):  
Effective Means ◽  
Operating Time ◽  
Seizure Freedom ◽  
Epileptogenic Zone ◽  
Complication Rates ◽  
Class Iii ◽  
Intracranial Hemorrhages ◽  
Robot Assisted ◽  
Pharmacoresistant Epilepsy ◽  
Electrode Implantation

OBJECTIVERobot-assisted stereoelectroencephalography (SEEG) is gaining popularity as a technique for localization of the epileptogenic zone (EZ) in children with pharmacoresistant epilepsy. Here, the authors describe their frameless robot-assisted SEEG technique and report preliminary outcomes and relative complications in children as compared to results with the Talairach frame–based SEEG technique.METHODSThe authors retrospectively analyzed the results of 19 robot-assisted SEEG electrode implantations in 17 consecutive children (age < 17 years) with pharmacoresistant epilepsy, and compared these results to 19 preceding SEEG electrode implantations in 18 children who underwent the traditional Talairach frame–based SEEG electrode implantation. The primary end points were seizure-freedom rates, operating time, and complication rates.RESULTSSeventeen children (age < 17 years) underwent a total of 19 robot-assisted SEEG electrode implantations. In total, 265 electrodes were implanted. Twelve children went on to have EZ resection: 4 demonstrated Engel class I outcomes, whereas 2 had Engel class II outcomes, and 6 had Engel class III–IV outcomes. Of the 5 patients who did not have resection, 2 underwent thermocoagulation. One child reported transient paresthesia associated with 2 small subdural hematomas, and 3 other children had minor asymptomatic intracranial hemorrhages. There were no differences in complication rates, rates of resective epilepsy surgery, or seizure freedom rates between this cohort and the preceding 18 children who underwent Talairach frame–based SEEG. The frameless robot-assisted technique was associated with shorter operating time (p < 0.05).CONCLUSIONSFrameless robot-assisted SEEG is a safe and effective means of identifying the EZ in children with pharmacoresistant partial epilepsy. Robot-assisted SEEG is faster than the Talairach frame–based method, and has equivalent safety and efficacy. The former, furthermore, facilitates more electrode trajectory possibilities, which may improve the localization of epileptic networks.

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