WS1.6. Focal-Onset Seizures: Surface Ictal EEG Recordings and Localization

2021 ◽  
Vol 132 (8) ◽  
pp. e52
Author(s):  
Byung In Lee
Keyword(s):  
Ictal Eeg ◽  
Eeg Recordings

scholarly journals Electroencephalographic Features of Temporal Lobe Epilepsy

2010 ◽  
Vol 37 (4) ◽  
pp. 439-448 ◽  
Author(s):  
Mohammed M. Jan ◽  
Mark Sadler ◽  
Susan R. Rahey
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Surgical Management ◽  
Temporal Lobe ◽  
Seizure Onset ◽  
Resective Surgery ◽  
Accurate Localization ◽  
Epileptiform Discharges ◽  
Ictal Eeg ◽  
Temporal Lobe Seizures ◽  
Eeg Recordings

Electroencephalography (EEG) is an important tool for diagnosing, lateralizing and localizing temporal lobe seizures. In this paper, we review the EEG characteristics of temporal lobe epilepsy (TLE). Several “non-standard” electrodes may be needed to further evaluate the EEG localization, Ictal EEG recording is a major component of preoperative protocols for surgical consideration. Various ictal rhythms have been described including background attenuation, start-stop-start phenomenon, irregular 2-5 Hz lateralized activity, and 5-10 Hz sinusoidal waves or repetitive epileptiform discharges. The postictal EEG can also provide valuable lateralizing information. Postictal delta can be lateralized in 60% of patients with TLE and is concordant with the side of seizure onset in most patients. When patients are being considered for resective surgery, invasive EEG recordings may be needed. Accurate localization of the seizure onset in these patients is required for successful surgical management.

Electroencephalographic Recordings of Focal Seizures in Patients Affected by Periventricular Nodular Heterotopia: Role of the Heterotopic Nodules in the Genesis of Epileptic Discharges

2004 ◽  
Vol 19 (3) ◽  
pp. 369-377
Author(s):  
Giorgio Battaglia ◽  
Silvana Franceschetti ◽  
Luisa Chiapparini ◽  
Elena Freri ◽  
Stefania Bassanini ◽  
...  
Keyword(s):  
Brain Regions ◽  
Periventricular Nodular Heterotopia ◽  
Focal Seizures ◽  
Ictal Eeg ◽  
Epileptic Discharges ◽  
Nodular Heterotopia ◽  
Eeg Recordings ◽  
Drug Resistant Epilepsy ◽  
Video Eeg

Patients affected by periventricular nodular heterotopia are frequently characterized by focal drug-resistant epilepsy. To investigate the role of periventricular nodules in the genesis of seizures, we analyzed the electroencephalographic (EEG) features of focal seizures recorded by means of video-EEG in 10 patients affected by different types of periventricular nodular heterotopia and followed for prolonged periods of time at the epilepsy center of our institute. The ictal EEG recordings with surface electrodes revealed common features in all patients: all seizures originated from the brain regions where the periventricular nodular heterotopia were located; EEG patterns recorded on the leads exploring the periventricular nodular heterotopia were very similar both at the onset and immediately after the seizure's end in all patients. Our data suggest that seizures are generated by abnormal anatomic circuitries, including the heterotopic nodules and adjacent cortical areas. The major role of heterotopic neurons in the genesis and propagation of epileptic discharges must be taken into account when planning surgery for epilepsy in patients with periventricular nodular heterotopia. ( J Child Neurol 2005;20:369—377).

scholarly journals Neonatal seizures and epilepsies

2014 ◽  
Vol 01 (02) ◽  
pp. 075-083 ◽  
Author(s):  
Kollencheri Vinayan ◽  
Solomon Moshé
Keyword(s):  
Motor Behavior ◽  
Neonatal Seizure ◽  
Neonatal Seizures ◽  
Immature Brain ◽  
Accurate Identification ◽  
Central Nervous System Dysfunction ◽  
Ictal Eeg ◽  
Neurologic Function ◽  
Eeg Recordings ◽  
Cerebral Insult

AbstractNeonatal seizure is the most frequent clinical manifestation of central nervous system dysfunction in the newborn. It is defined as a paroxysmal alteration in neurologic function that include motor, behavior and/or autonomic functions occurring in the first 28 days after birth of a term neonate or before 44 weeks of gestational age in a preterm infant. Seizures in the presence of encephalopathy are the most important clinical pattern of an acute cerebral insult in the immature brain. Chronic epileptic disorders very rarely may have their onset in the neonatal period and may persist well into infancy and later childhood. Structural brain defects and metabolic disorders constitute a substantial proportion of this group. Ictal EEG recordings remain the gold standard for the accurate identification of neonatal seizures of cortical origin and for the distinction from non-epileptic paroxysmal events. This review focuses on the electroclinical patterns of neonatal seizures and epilepsies with an emphasis on the classification and terminologies. The current therapeutic options are also highlighted briefly.

scholarly journals Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study

2007 ◽  
Vol 2007 ◽  
pp. 1-10 ◽  
Author(s):  
Maarten De Vos ◽  
Lieven De Lathauwer ◽  
Bart Vanrumste ◽  
Sabine Van Huffel ◽  
W. Van Paesschen
Keyword(s):  
Simulation Study ◽  
Seizure Activity ◽  
Canonical Decomposition ◽  
Scalp Eeg ◽  
Activity Frequency ◽  
Ictal Eeg ◽  
Single Dipole ◽  
Eeg Recordings ◽  
Frequency Changes ◽  
Dipole Localisation

Long-term electroencephalographic (EEG) recordings are important in the presurgical evaluation of refractory partial epilepsy for the delineation of the ictal onset zones. In this paper, we introduce a new concept for an automatic, fast, and objective localisation of the ictal onset zone in ictal EEG recordings. Canonical decomposition of ictal EEG decomposes the EEG in atoms. One or more atoms are related to the seizure activity. A single dipole was then fitted to model the potential distribution of each epileptic atom. In this study, we performed a simulation study in order to estimate the dipole localisation error. Ictal dipole localisation was very accurate, even at low signal-to-noise ratios, was not affected by seizure activity frequency or frequency changes, and was minimally affected by the waveform and depth of the ictal onset zone location. Ictal dipole localisation error using 21 electrodes was around 10.0 mm and improved more than tenfold in the range of 0.5–1.0 mm using 148 channels. In conclusion, our simulation study of canonical decomposition of ictal scalp EEG allowed a robust and accurate localisation of the ictal onset zone.

scholarly journals P.052 Benign spasms of infancy - a mimicker of infantile epileptic disorders

2019 ◽  
Vol 46 (s1) ◽  
pp. S28
Author(s):  
J Ghossein ◽  
D Pohl
Keyword(s):  
Epileptiform Activity ◽  
Positive Family History ◽  
Myoclonic Epilepsy ◽  
First Year ◽  
Neurodevelopmental Outcomes ◽  
Epileptic Spasms ◽  
Ictal Eeg ◽  
Interictal Eeg ◽  
Eeg Recordings ◽  
First Year Of Life

Background: Benign spasms of infancy (BSI), previously described as benign non-epileptic infantile spasms or benign myoclonus of early infancy, are non-epileptic movements manifesting during the first year of life and spontaneously resolving in the second year of life. BSI are characterized by spasms typically lasting 1-2 seconds, involving to varying degrees the head, neck, trunk, shoulders and upper extremities. Ictal and interictal EEG recordings are normal. BSI are not associated with developmental retardation and do not require treatment. Distinction between BSI and infantile epileptic disorders, such as epileptic spasms or myoclonic epilepsy of infancy, can be challenging given the clinical similarities. Moreover, interictal EEGs can be normal in all conditions. Epileptic spasms and myoclonic epilepsy require timely treatment to improve neurodevelopmental outcomes. Methods: We describe a 6-month old infant presenting with spasm-like movements. His paroxysms as well as a positive family history for epileptic spasms were in keeping with a likely diagnosis of West syndrome. Results: Surprisingly, ictal video EEG did not reveal epileptiform activity, and suggested a diagnosis of BSI. Conclusions: We emphasize that ictal EEG is the gold standard for classification of infantile paroxysms as either epileptic or non-epileptic, thereby avoiding overtreatment of BSI and facilitating timely targeted treatment of infantile epilepsies.

scholarly journals B.03 Registered EEG technologists can accurately identify ictal and interictal epileptiform patterns on routine EEG

2018 ◽  
Vol 45 (s2) ◽  
pp. S12-S12
Author(s):  
Y Wu ◽  
S Weiss ◽  
C Hahn
Keyword(s):  
Retrospective Study ◽  
Sensitivity And Specificity ◽  
Background Activity ◽  
Final Report ◽  
Technical Aspects ◽  
Epileptiform Discharges ◽  
Ictal Eeg ◽  
Eeg Recordings

Background: Registered EEG technologists (RETs) are trained in both the technical aspects of EEG and in preliminary EEG interpretation. However, there is little research evaluating the accuracy of EEG interpretation by RETs. Methods: Retrospective study of consecutive routine EEG recordings performed at SickKids Hospital. Preliminary reports by RETs and final reports by neurophysiologists were compared in 5 domains: background activity, focal abnormalities, ictal and inter-ictal epileptiform discharges and summary. Results: 500 EEG recordings were analyzed. Sensitivity and specificity of RET reports was high for the assessment of background (85%, 93%), focal slowing (84%, 93%) and inter-ictal epileptiform discharges (92%, 90%). RET reports identified ictal EEG patterns in 32 cases vs. 29 cases identified by neurophysiologists. RET reports were 100% accurate for noting no EEG change for all of 11 cases with non-epileptic events. Conclusions: Preliminary EEG reports by RETs were sensitive and specific for all EEG domains analyzed. In the majority of cases, the preliminary interpretation made by the RET was concordant with the final report of the neurophysiologist. Given these findings, RETs may be able to participate in the screening of routine EEG recordings in order to enhance the productivity of busy EEG laboratories.

Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG

2015 ◽  
Vol 25 (06) ◽  
pp. 1550020 ◽  
Author(s):  
Yanli Zhang ◽  
Weidong Zhou ◽  
Shasha Yuan
Keyword(s):  
Multifractal Analysis ◽  
Detection System ◽  
Transient Behavior ◽  
Seizure Detection ◽  
Relevance Vector Machine ◽  
Detection Technology ◽  
Ictal Eeg ◽  
Interictal Eeg ◽  
Eeg Recordings ◽  
Event Based

Automatic seizure detection technology is of great significance for long-term electroencephalogram (EEG) monitoring of epilepsy patients. The aim of this work is to develop a seizure detection system with high accuracy. The proposed system was mainly based on multifractal analysis, which describes the local singular behavior of fractal objects and characterizes the multifractal structure using a continuous spectrum. Compared with computing the single fractal dimension, multifractal analysis can provide a better description on the transient behavior of EEG fractal time series during the evolvement from interictal stage to seizures. Thus both interictal EEG and ictal EEG were analyzed by multifractal formalism and their differences in the multifractal features were used to distinguish the two class of EEG and detect seizures. In the proposed detection system, eight features (α0, αmin, αmax, Δα, f(α min ), f(α max ), Δf and R) were extracted from the multifractal spectrums of the preprocessed EEG to construct feature vectors. Subsequently, relevance vector machine (RVM) was applied for EEG patterns classification, and a series of post-processing operations were used to increase the accuracy and reduce false detections. Both epoch-based and event-based evaluation methods were performed to appraise the system's performance on the EEG recordings of 21 patients in the Freiburg database. The epoch-based sensitivity of 92.94% and specificity of 97.47% were achieved, and the proposed system obtained a sensitivity of 92.06% with a false detection rate of 0.34/h in event-based performance assessment.

scholarly journals AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 30 ◽  
Author(s):  
Shennan Aibel Weiss ◽  
Ali A Asadi-Pooya ◽  
Sitaram Vangala ◽  
Stephanie Moy ◽  
Dale H Wyeth ◽  
...  
Keyword(s):  
Onset Time ◽  
Clinical Findings ◽  
Artifact Reduction ◽  
Seizure Onset ◽  
Intra Class Correlation ◽  
Ictal Eeg ◽  
Reduction Methods ◽  
Eeg Recordings ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (p<0.01). Fewer readers could lateralize seizure-onset (p<0.05). The confidence measures of the assignments were low (probable-unlikely), but increased using AR2 (p<0.05). The ICC for identifying the time of seizure-onset was 0.15 (95% confidence interval (CI), 0.11-0.18) using AR1 and 0.26 (95% CI 0.21-0.30) using AR2.  The EEG interpretations were often consistent with behavioral, neurophysiological, and neuro-radiological findings, with left sided assignments correct in 95.9% (CI 85.7-98.9%, n=4) of cases using AR2, and 91.9% (77.0-97.5%) (n=4) of cases using AR1. Conclusions: EEG artifact reduction methods for localizing seizure-onset does not result in high rates of interpretability, reader confidence, and inter-reader agreement. However, the assignments by groups of readers are often congruent with other clinical data. Utilization of the AR2 software method may improve the validity of ictal EEG artifact reduction.

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