Interictal Electroencephalography (EEG) Findings in Children with Epilepsy and Bilateral Brain Lesions on Magnetic Resonance Imaging (MRI)

Zubčević, Smail; Milos, Maja; Čatibušić, Feriha; Uzicanin, Sajra; Krdzalic, B.

doi:10.5455/aim.2015.23.343-346

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…Abnormal findings using MRI were detected in 68% of patients with multifocal IED. Zubcevic et al demonstrated that 55.9% of the patients had bilateral IED in case of bilateral lesions observed by MRI, but clearly focal IED could also be observed [ 7 ]. Multifocal discharges in the present study were related to higher rates of abnormalities on the basis of the MRI findings.…”

Section: Discussionmentioning

confidence: 99%

“…This was similar with the previous reports, that indicated 55.9% bilateral epileptiform discharges in cases with bilateral lesions evidenced by brain MRI. It was postulated that some of the findings detected by MRI are not the exact cause of epilepsy but are coincidental with other abnormalities, such as hypomyelination and delayed myelination often present in patients with metabolic disorders as a cause of epilepsy [ 7 , 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…EEG findings in patients with bilateral brain lesions were demonstrated to reveal approximately 55% of bilateral interictal discharges (IED). However, in a study, focal IED were found to be unrelated to lesions detected by MRI [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Magnetic Resonance Imaging and Interictal Electroencephalography Findings in Newly Diagnosed Epileptic Children

Dirik

Şanlıdağ

2018

JCM

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Introduction: Epilepsy is one of the most frequently diagnosed chronic neurological disorders in children. Diagnosis is often based on seizure history and electroencephalography (EEG) assessment. Magnetic resonance imaging (MRI) is recommended for etiologic workup and intervention requirements. We aimed to detect by MRI if focal structural abnormalities are present in the brain in relation to interictal epileptiform discharges (IED). Material and Methods: The study was designed retrospectively. The data were collected from patients admitted to Near East University, Department of Pediatric Neurology, who were aged between 3 months and 18 years and who were diagnosed with epilepsy. The cases considered in the current study, however, were patients that had an EEG record prior to initiating treatment and an MRI within the first six months following diagnosis. Results: Among 222 patients, 212 (95.5%) had IED, and 92 (41.4%) had abnormal MRI results. The most frequently seen abnormalities detected by MRI were encephalomalacia, hydrocephaly, and atrophy. Among patients who had IED, the ones with multifocal IED were documented to have a statistically significant higher rate of abnormalities in MRI scans. In other patients, IED had no significant correlation with structural lesions detected by MRI. Conclusion: IED can be unrelated to MRI findings. Focal IED were not statistically concordant with the structural lesions detected by MRI. However, for the cases with multifocal discharges revealed by interictal EEG, the rate of abnormalities detected using MRI was 68%. Therefore, the likelihood of detecting abnormalities using MRI in patients with multifocal IED does support the necessity of the use of MRI in early diagnosis stages.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Magnetic Resonance Imaging and Interictal Electroencephalography Findings in Newly Diagnosed Epileptic Children

Dirik

Şanlıdağ

2018

JCM

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“…The ictal EEG recordings were considered optimal in the diagnosis of epilepsy; however, it can be challenging to acquire these recordings in real-life scenarios. As a result, interictal epileptiform activities, which are associated with epilepsy, were used as diagnostic tools [25].…”

Section: Databasesmentioning

confidence: 99%

A Novel Automated Empirical Mode Decomposition (EMD) Based Method and Spectral Feature Extraction for Epilepsy EEG Signals Classification

2023

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The increasing incidence of epilepsy has led to the need for automatic systems that can provide accurate diagnoses in order to improve the life quality of people suffering from this neurological disorder. This paper proposes a method to automatically classify epilepsy types using EEG recordings from two databases. This approach uses the spectral power density of intrinsic mode functions (IMFs) that are obtained through the empirical mode decomposition (EMD) of EEG signals. The spectral power density of IMFs has been applied as features for the classification of focal and non-focal, as well as of focal and generalized EEG signals. The data are then classified using K-nearest Neighbor (KNN) and Naïve Bayes (NB) classifiers. The focal and non-focal data were classified with high accuracy, with KNN and NB classifiers achieving a maximum classification rate of 99.90% and 99.80%, respectively. Focal and generalized epilepsy data were classified with high rates of accuracy during wakefulness and sleep stages, with KNN achieving a maximum rate of 99.49% and NB achieving 99.20%. This method shows significant improvements in the classification of EEG signals in epilepsy compared to previous studies. It could potentially aid clinical decisions for epilepsy patients.

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“…These discharges may be either generalized or focal in distribution and help to classify whether a focal or generalized seizure disorder is present. 1,2 EEG can help identify seizures type and localization of epileptic focus. Also, some patterns of EEG are predictive of patients who can develop lateonset seizures, in addition, 3 epilepsy is a condition of chronic, recurring seizures and its most disabling aspect is the unpredictability of when and where the next seizure will occur.…”

Section: Introductionmentioning

confidence: 99%

Electro encephalography screening of suspected epileptic patients in Erbil City

Hummadi

2019

Zanco J. Med. Sci.

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non-epileptic patients through electroencephalography investigation and determine its relationship with certain socio-demographic and clinical characteristics. Methods: In this cross-sectional study included 400 cases (205 males and 195 females) with a mean age ± SD of 25 ± 14 years, referred to the private neurology clinic, Soran Medical Center between April 2013 and March 2017 for attacks of abnormal movements and or disturbed level of consciousness. Results: Age-related prevalence of epilepsy showed a significantly higher prevalence (P = 0.001) of the disease among those who were 20 years and more. Epileptic patients showed significantly (P = 0.001) higher sharp slow waves and sharp waves, which were mainly dominated by parietal and occipital regions of the brain. Electroencephalography finding showed higher Beta rhythm followed by Alpha and Delta rhythms (73.5%, 14.3% and 9.0%, respectively), Patients with epilepsy (generalized and focal) had shown best rhythm adoption in Beta rhythm (75.2% and 70.2%, respectively), followed by Alpha rhythm (13.2% and 14.8%, respectively). Focal to bilateral (secondary generalized) and generalized motor epilepsy were the most common diagnosis among the epileptic cases (45.4% and 43.4%, respectively). The overall sensitivity of electroencephalography was 67.8%, and specificity was 63.5%. Conclusion: The electroencephalography showed good sensitivity and specificity in diagnosing suspected epileptic patients, with interesting higher sensitivity than specificity. Not only the abnormal discharges, but the background dominating activity and the best rhythm adoption can help in the diagnosis of epilepsy.

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Interictal Electroencephalography (EEG) Findings in Children with Epilepsy and Bilateral Brain Lesions on Magnetic Resonance Imaging (MRI)

Cited by 3 publications

References 19 publications

Magnetic Resonance Imaging and Interictal Electroencephalography Findings in Newly Diagnosed Epileptic Children

Magnetic Resonance Imaging and Interictal Electroencephalography Findings in Newly Diagnosed Epileptic Children

A Novel Automated Empirical Mode Decomposition (EMD) Based Method and Spectral Feature Extraction for Epilepsy EEG Signals Classification

Electro encephalography screening of suspected epileptic patients in Erbil City

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