Characterizing EEG Cortical Dynamics and Connectivity with Responses to Single Pulse Electrical Stimulation (SPES)

Objectives: To model cortical connections in order to characterize their oscillatory behavior and role in the generation of spontaneous electroencephalogram (EEG). Methods: We studied averaged responses to single pulse electrical stimulation (SPES) from the non-epileptogenic hemisphere of five patients assessed with intracranial EEG who became seizure free after contralateral temporal lobectomy. Second-order control system equations were modified to characterize the systems generating a given response. SPES responses were modeled as responses to a unit step input. EEG power spectrum was calculated on the 20[Formula: see text]s preceding SPES. Results: 121 channels showed responses to 32 stimulation sites. A single system could model the response in 41.3% and two systems were required in 58.7%. Peaks in the frequency response of the models tended to occur within the frequency range of most activity on the spontaneous EEG. Discrepancies were noted between activity predicted by models and activity recorded in the spontaneous EEG. These discrepancies could be explained by the existence of alpha rhythm or interictal epileptiform discharges. Conclusions: Cortical interactions shown by SPES can be described as control systems which can predict cortical oscillatory behavior. The method is unique as it describes connectivity as well as dynamic interactions.

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Can single pulse electrical stimulation provoke responses similar to spontaneous interictal epileptiform discharges?

Clinical Neurophysiology ◽

10.1016/j.clinph.2013.11.019 ◽

2014 ◽

Vol 125 (7) ◽

pp. 1306-1311 ◽

Cited By ~ 17

Author(s):

Dinesh Nayak ◽

Antonio Valentín ◽

Richard P. Selway ◽

Gonzalo Alarcón

Keyword(s):

Electrical Stimulation ◽

Single Pulse ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges

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Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG

Healthier Lives, Digitally Enabled - Studies in Health Technology and Informatics ◽

10.3233/shti210012 ◽

2021 ◽

Author(s):

Duong Nhu ◽

Mubeen Janmohamed ◽

Lubna Shakhatreh ◽

Ofer Gonen ◽

Patrick Kwan ◽

...

Keyword(s):

Royal Melbourne Hospital ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Model Variant ◽

Eeg Recordings ◽

Generalized Epilepsy ◽

Electroencephalogram Eeg ◽

Health Hospital ◽

Epilepsy Monitoring

Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the literature are small (n≤100) and collected from single clinical centre, limiting the generalization across different devices and settings. To better automate IED detection, we cross-evaluated a Resnet architecture on 2 sets of routine EEG recordings from patients with idiopathic generalized epilepsy collected at the Alfred Health Hospital and Royal Melbourne Hospital (RMH). We split these EEG recordings into 2s windows with or without IED and evaluated different model variants in terms of how well they classified these windows. The results from our experiment showed that the architecture generalized well across different datasets with an AUC score of 0.894 (95% CI, 0.881–0.907) when trained on Alfred’s dataset and tested on RMH’s dataset, and 0.857 (95% CI, 0.847–0.867) vice versa. In addition, we compared our best model variant with Persyst and observed that the model was comparable.

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A STUDY ON COMPARATIVE YIELDS OF STANDARD SHORT TERM ELECTROENCEPHALOGRAM AND LONG TERM ELECTROENCEPHALOGRAM RECORDING IN SUSPECTED EPILEPSY PATIENTS

Pakistan Armed Forces Medical Journal ◽

10.51253/pafmj.v71i5.5921 ◽

2021 ◽

Vol 71 (5) ◽

pp. 1727-31

Author(s):

Saima Shafait ◽

Wasim Alamgir ◽

Imran Ahmad ◽

Saeed Arif ◽

Jahanzeb Liaqat ◽

...

Keyword(s):

Diagnostic Yield ◽

Extended Period ◽

Final Analysis ◽

Epileptiform Discharge ◽

Military Hospital ◽

Recording Time ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Interictal Epileptiform Discharge ◽

Electroencephalogram Eeg

Objective: To compare the yield of interictal epileptiform discharges on prolonged (1-2 hours) electroencephalogram (EEG) as compared to standard routine (30 minutes) electroencephalogram (EEG). Study Design: Comparative observational study. Place and Duration of Study: Pak Emirates Military Hospital, Rawalpindi from Oct 2019 to Sep 2020. Methodology: A total of 364 outdoor patients with suspected epilepsy were recruited for the study. Out of these 55 electroencephalograms were excluded after applying exclusion criteria and 309 were included for final analysis. Electro-encephalograms were recorded using a 10-20 international system of electrode placement. The duration of each standard electroencephalogram was 30 minutes. It was followed by recording for an extended period of 60 minutes at least. The time to the appearance of the first abnormal interictal epileptiform discharge was noted. For analytical purposes, epileptiform discharges were classified as “early” if they appeared within the first 30 minutes and as “late” if appeared afterward. All electro-encephalograms were evaluated independently by two neurologists. Results: A total of 309 electroencephalograms were included for final analysis. Interictal epileptiform discharges were seen in 48 (15.6%) recordings. The mean time to appearance of first interictal epileptiform discharge was 14.6 ± 19.09 minutes. In 36 (11.7%) cases, discharges appeared early (within the first 30 minutes) whereas in the remaining 12 (3.9%) cases, discharges appeared late. This translates into a 33% increase in the diagnostic yield of electroencephalogram with an extended period of recording. Conclusion: Extending the electroencephalogram recording time results in a significantly better diagnostic yield of outdoor electroencephalogram.

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Prospective evaluation of interrater agreement between EEG technologists and neurophysiologists

Scientific Reports ◽

10.1038/s41598-021-92827-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Isabelle Beuchat ◽

Senubia Alloussi ◽

Philipp S. Reif ◽

Nora Sterlepper ◽

Felix Rosenow ◽

...

Keyword(s):

Interrater Agreement ◽

Prospective Evaluation ◽

False Negatives ◽

Predictive Values ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Eeg Recordings ◽

Taking Action ◽

Electroencephalogram Eeg ◽

Eeg Pattern

AbstractWe aim to prospectively investigate, in a large and heterogeneous population, the electroencephalogram (EEG)-reading performances of EEG technologists. A total of 8 EEG technologists and 5 certified neurophysiologists independently analyzed 20-min EEG recordings. Interrater agreement (IRA) for predefined EEG pattern identification between EEG technologists and neurophysiologits was assessed using percentage of agreement (PA) and Gwet-AC1. Among 1528 EEG recordings, the PA [95% confidence interval] and interrater agreement (IRA, AC1) values were as follows: status epilepticus (SE) and seizures, 97% [96–98%], AC1 kappa = 0.97; interictal epileptiform discharges, 78% [76–80%], AC1 = 0.63; and conclusion dichotomized as “normal” versus “pathological”, 83.6% [82–86%], AC1 = 0.71. EEG technologists identified SE and seizures with 99% [98–99%] negative predictive value, whereas the positive predictive values (PPVs) were 48% [34–62%] and 35% [20–53%], respectively. The PPV for normal EEGs was 72% [68–76%]. SE and seizure detection were impaired in poorly cooperating patients (SE and seizures; p < 0.001), intubated and older patients (SE; p < 0.001), and confirmed epilepsy patients (seizures; p = 0.004). EEG technologists identified ictal features with few false negatives but high false positives, and identified normal EEGs with good PPV. The absence of ictal features reported by EEG technologists can be reassuring; however, EEG traces should be reviewed by neurophysiologists before taking action.

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Predictors of outcome of anterior temporal lobectomy for intractable epilepsy

Neurology ◽

10.1212/wnl.51.2.465 ◽

1998 ◽

Vol 51 (2) ◽

pp. 465-471 ◽

Cited By ~ 238

Author(s):

K. Radhakrishnan ◽

E. L. So ◽

P. L. Silbert ◽

C. R. Jack ◽

G. D. Cascino ◽

...

Keyword(s):

Seizure Control ◽

Hippocampal Formation ◽

Intractable Epilepsy ◽

Temporal Lobectomy ◽

Anterior Temporal Lobectomy ◽

Excellent Outcome ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Univariate Analyses

Objective: To identify presurgical and postsurgical factors that are independently predictive of the outcome of anterior temporal lobectomy (ATL) for intractable epilepsy.Background: There have been reports of prognostic studied 175 consecutive ATL patients who had at least 2 years of postsurgical follow-up. Significant factors on univariate analyses were subjected to stepwise logistic regression analysis.Results: On univariate analyses, two presurgical conditions were significantly associated with excellent seizure control at last follow-up: (1) unilateral hippocampal formation atrophy as detected on MRI and (2) all scalp interictal epileptiform discharges concordant with the location of ictal onset(p < 0.05). Three postsurgical factors that occurred during the first year were associated with excellent seizure outcome: the absence of interictal epileptiform discharges at 3 months, complete seizure control, and having only nondisabling seizures for those who did not become seizure free. Logistic regression analysis revealed the following to be independently predictive of excellent seizure control: MRI-detected unilateral hippocampal formation atrophy, concordant interictal epileptiform discharges, complete seizure control during the first postsurgical year, and having only nondisabling seizures during the first postsurgical year for those who did not become seizure free.Conclusions: Presurgical identification of unilateral hippocampal formation atrophy, or of interictal epileptiform discharges that are all concordant with the location of ictal onset, predict excellent outcome of ATL. However, the probability of excellent outcome is highest (94%) when both factors are present.

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Reiterating the role of corpus callosum in generalization of interictal and ictal epileptiform discharges: a case report with post-callosotomy intracranial electroencephalography in Lennox–Gastaut syndrome

Epilepsia and paroxyzmal conditions ◽

10.17749/2077-8333/epi.par.con.2021.086 ◽

2021 ◽

Vol 13 (3) ◽

pp. 249-253

Author(s):

S. Gopinath ◽

A. Pillai ◽

A. G. Diwan ◽

J. V. Pattisapu ◽

K. Radhakrishnan

Keyword(s):

Corpus Callosum ◽

Epileptic Encephalopathy ◽

Drop Attacks ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Eeg Abnormalities ◽

Before And After ◽

Intracranial Electroencephalography ◽

Electroencephalogram Eeg

Lennox–Gastaut syndrome (LGS) is an epileptic encephalopathy characterized by delayed mental development and intractable multiple seizure types, predominantly tonic. Drop attacks are the commonest and the most disabling type of seizures. Resective surgery is often not possible in LGS as the electroencephalogram (EEG) abnormalities are usually multifocal and generalized, and magnetic resonance image is often either normal or multilesional. We report a case of LGS with bilateral parieto-occipital gliosis where EEG before and after callosotomy demonstrated synchronized bilateral interictal epileptiform discharges and ictal discharges becoming desynchronized and running down. This phenomenon emphasizes the role of the corpus callosum in secondary bilateral synchrony.

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Effects of Interictal Epileptiform Discharges on Cognition

Journal of Pediatric Epilepsy ◽

10.1055/s-0038-1676847 ◽

2018 ◽

Vol 07 (03) ◽

pp. 082-088 ◽

Cited By ~ 1

Author(s):

Aag Jennekens-Schinkel ◽

Joost Meekes

Keyword(s):

Cognitive Impairment ◽

Cognitive Deficits ◽

Cognitive Deficit ◽

Single Case ◽

Careful Monitoring ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Single Case Designs ◽

Lower Intelligence ◽

Electroencephalogram Eeg

AbstractInterictal epileptiform discharges (IEDs) are brief events (typically < 1 second, although some authors use a cutoff of up to 3 seconds) in the electroencephalogram (EEG), which are not accompanied by any overt change in behavior or consciousness. IEDs are associated with both chronic cognitive deficits and transient cognitive impairment (TCI). Higher IED load correlates with lower intelligence quotient (IQ) and poorer performance on tests of memory and executive function. TCI has mainly been observed during tasks that place high demands on attention, visuomotor speed, and working memory. There is evidence that IEDs may also directly interfere with episodic memory. Despite the evidence for associations between IEDs and cognitive dysfunction, there is currently scant evidence that IEDs, in fact, cause chronic cognitive deficit in humans. Although causality appears likely in the case of TCI, even here the conditions under which IEDs affect cognition are unclear. The evidence in favor of treating IEDs with medication is very limited. Attempts to treat IEDs should only be made when it is clear that cognitive impairment interferes with activities or participation. Such attempts require strong single-case designs and careful monitoring of both the EEG and cognitive function to establish the efficacy of treatment in individual patients.

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