Focal interictal epileptiform discharges in idiopathic generalized epilepsy

2016 ◽  
Vol 37 (7) ◽  
pp. 1071-1077 ◽  
Author(s):  
Eman H. Esmail ◽  
Amani M. Nawito ◽  
Dalia M. Labib ◽  
Mye A. Basheer
Keyword(s):  
Idiopathic Generalized Epilepsy ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Generalized Epilepsy

scholarly journals CLINICAL AND EEG PREDICTORS OF INCREASED FREQUENCY OF GENERALIZED SEIZURES IN GESTATIONAL AND POSTGRAVIDARUM PERIOD

2013 ◽  
Vol 12 (5) ◽  
pp. 24-30
Author(s):  
O. V. Grebenyuk ◽  
M. V. Svetlik ◽  
V. M. Alifiriva ◽  
I. D. Yevtushenko ◽  
Yu. A. Bochkov ◽  
...  
Keyword(s):  
Idiopathic Generalized Epilepsy ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Generalized Seizures ◽  
Generalized Epilepsy

We investigated the predictors of increased seizures’ frequency among women with various forms of idiopathic generalized epilepsies (IGE) in the gestational and postgravid period. We observed 41 patients with Idiopathic generalized epilepsy (IGE) before, during and after pregnancy. During the period of observation EEG was recorded in the states of relaxed and active awakeness and sleeping. Patients, who have stopped to take anticonvulsants before pregnancy, have had increased frequency of seizures, more than1 ina year, with interictal epileptiform discharges in a state of relaxed and active awakeness. Increased seizures after pregnancy was observed in patients with interictal epileptiform discharges during sleep. The results can be used during the preparation of pregravid women with IGE. 

scholarly journals Automated Inter-Ictal Epileptiform Discharge Detection from Routine EEG

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.

scholarly journals Interictal Activity Is Associated With Slower Binocular Rivalry in Idiopathic Generalized Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiaonan Wu ◽  
Wei Ding ◽  
Xing Ye ◽  
Qiang Wei ◽  
Xinyi Lv ◽  
...  
Keyword(s):  
Standard Error ◽  
Cognitive Assessment ◽  
Characteristic Curve ◽  
Montreal Cognitive Assessment ◽  
Alternation Rate ◽  
Assessment Score ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Perceptual Alternation ◽  
Generalized Epilepsy

Objective: Perceptual alternations evoked by binocular rivalry (BR) reflect cortical dynamics strongly dependent on the excitatory–inhibitory balance, suggesting potential utility as a biomarker for epileptogenesis. Therefore, we investigated the characteristics of BR in patients with idiopathic generalized epilepsy (IGE) and potential associations with clinical variables.Methods: Sixty-two healthy controls (HCs) and 94 IGE patients completed BR task. Perceptual alternation rates were compared between HC and IGE groups as well as among the HC group and IGE patients stratified according to the presence or absence of interictal activity on the ambulatory electroencephalogram (EEG), termed the abnormal ambulatory EEG group (AB-AEEG, n = 64) and normal ambulatory EEG group (N-AEEG, n = 30), respectively.Results: The IGE patients demonstrated a slower rate of BR perceptual alternation than HC subjects (t = −4.364, p < 0.001). The alternation rate also differed among the HC, AB-AEEG, and N-AEEG groups (F = 44.962, df = 2, p < 0.001), and post hoc comparisons indicated a significantly slower alternation rate in the AB-AEEG group compared with the N-AEEG and HC groups (0.28 vs. 0.46, and 0.43 Hz). Stepwise linear regression revealed positive correlations between the BR alternation rate and both the ambulatory EEG status (β, 0.173; standard error, 0.022 p < 0.001) and Montreal Cognitive Assessment score (β, 0.013; standard error, 0.004; p = 0.003). Receiver operating characteristic curve analysis of the BR alternation rate distinguished AB-AEEG from N-AEEG subjects with 90.00% sensitivity and 76.90% specificity (area under the curve = 0.881; 95% confidence interval = 0.801– 0.961, cut-off = 0.319). Alternatively, Montreal Cognitive Assessment score did not accurately distinguish AB-AEEG from N-AEEG subjects and the area under the receiver operating characteristic curve combining the BR alternation rate and Montreal Cognitive Assessment score was not markedly larger than that of the BR alternation rate alone (0.894, 95% confidence interval = 0.822–0.966, p < 0.001). K-fold cross-validation was used to evaluate the predictive performance of BR alternation rate, MoCA score, and the combination of both, which yielded average AUC values of 0.870, 0.584 and 0.847, average sensitivity values of 89.36, 92.73, and 91.28%, and average specificity values of 62.25, 13.42, and 61.78%, respectively. The number of interictal epileptiform discharges was significantly correlated with the alternation rate in IGE patients (r = 0.296, p = 0.018). A forward stepwise linear regression model identified the number of interictal epileptiform discharges (β, 0.001; standard error, 0.001; p = 0.025) as an independent factor associated with BR alternation rate in these patients.Conclusion: These results suggest that interictal epileptiform discharges are associated with disruptions in perceptual awareness, and that the BR may be a useful auxiliary behavioral task to diagnosis and dynamically monitor IGE patients with interictal discharge.

scholarly journals Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Won-Du Chang ◽  
Ho-Seung Cha ◽  
Chany Lee ◽  
Hoon-Chul Kang ◽  
Chang-Hwan Im
Keyword(s):  
Automatic Identification ◽  
Sharp Waves ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Detection And Identification ◽  
Characteristic Signal ◽  
Generalized Epilepsy ◽  
Scalp Electroencephalogram ◽  
Patients With Epilepsy ◽  
Epileptogenic Foci

Ictal epileptiform discharges (EDs) are characteristic signal patterns of scalp electroencephalogram (EEG) or intracranial EEG (iEEG) recorded from patients with epilepsy, which assist with the diagnosis and characterization of various types of epilepsy. The EEG signal, however, is often recorded from patients with epilepsy for a long period of time, and thus detection and identification of EDs have been a burden on medical doctors. This paper proposes a new method for automatic identification of two types of EDs, repeated sharp-waves (sharps), and runs of sharp-and-slow-waves (SSWs), which helps to pinpoint epileptogenic foci in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). In the experiments with iEEG data acquired from a patient with LGS, our proposed method detected EDs with an accuracy of 93.76% and classified three different signal patterns with a mean classification accuracy of 87.69%, which was significantly higher than that of a conventional wavelet-based method. Our study shows that it is possible to successfully detect and discriminate sharps and SSWs from background EEG activity using our proposed method.

scholarly journals Localization of Epileptic Foci Based on Simultaneous EEG–fMRI Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Seyyed Mostafa Sadjadi ◽  
Elias Ebrahimzadeh ◽  
Mohammad Shams ◽  
Masoud Seraji ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Brain Activity ◽  
Epileptiform Activity ◽  
Brain Regions ◽  
Fmri Data ◽  
Clinical Approach ◽  
Hemodynamic Response Function ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Surgical Evaluation ◽  
Generalized Epilepsy

Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) enables a non-invasive investigation of the human brain function and evaluation of the correlation of these two important modalities of brain activity. This paper explores recent reports on using advanced simultaneous EEG–fMRI methods proposed to map the regions and networks involved in focal epileptic seizure generation. One of the applications of EEG and fMRI combination as a valuable clinical approach is the pre-surgical evaluation of patients with epilepsy to map and localize the precise brain regions associated with epileptiform activity. In the process of conventional analysis using EEG–fMRI data, the interictal epileptiform discharges (IEDs) are visually extracted from the EEG data to be convolved as binary events with a predefined hemodynamic response function (HRF) to provide a model of epileptiform BOLD activity and use as a regressor for general linear model (GLM) analysis of the fMRI data. This review examines the methodologies involved in performing such studies, including techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. It then discusses the results reported for patients with primary generalized epilepsy and patients with different types of focal epileptic disorders. An important matter that these results have brought to light is that the brain regions affected by interictal epileptic discharges might not be limited to the ones where they have been generated. The developed methods can help reveal the regions involved in or affected by a seizure onset zone (SOZ). As confirmed by the reviewed literature, EEG–fMRI provides information that comes particularly useful when evaluating patients with refractory epilepsy for surgery.

scholarly journals Different Types of Epilepsy Based on Clinical and Electroencephalographic (EEG) Findings: Experience at Referral Neuroscience Hospital in Bangladesh

2018 ◽  
Vol 3 (1) ◽  
pp. 3-6
Author(s):  
Mohammad Enayet Hussain ◽  
AFM Al Masum Khan ◽  
Md Nahidul Islam ◽  
Md Ferdous Mian ◽  
Md Bakhtiar Azam ◽  
...  
Keyword(s):  
Clinical History ◽  
Cross Sectional Study ◽  
Epilepsy Syndrome ◽  
Cross Sectional ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Different Types ◽  
Male Patients ◽  
Generalized Epilepsy ◽  
Structured Questionnaire

Background: A good history and a standard EEG recording help establish most of the epilepsy syndromes.Objective: The objective of this study was to establish different epilepsy syndromes on the basis of history and EEG in the clinically suspected seizure events.Methodology: This cross-sectional study was carried out in the neurophysiology laboratory of National Institute of Neurosciences & Hospital, Dhaka, Bangladesh from January 2013 to December 2015, which included 2549 patients. EEG was obtained through surface scalp electrodes according to international 10/20 system. Patient and their attendants were interviewed using a semi structured questionnaire. The EEG findings, clinical history and in appropriate cases the neuroimaging, CSF and hematological findings were then correlated.Result: Among the 2549 patients most were children (39.8% less than 10 years old) and young adult (30.63% in 11 to 20 years age group). Male patients outnumbered female (63% and 36 % respectively). The overall sensitivity of EEG in yielding abnormal interictal epileptiform discharges was 42%. About 32% of total 2549 patients were diagnosed as localization-related epilepsy (LRE), 5% idiopathic generalized epilepsy (IGE), 1.41% was Epileptic encephalopathy.Conclusion: In conclusion EEG is helpful in classifying the types of seizure, aids in defining the epilepsy syndrome, predicting the outcome and assists in management of patients.Journal of National Institute of Neurosciences Bangladesh, 2017;3(1): 3-6

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