Familial partial epilepsy with variable foci: A new partial epilepsy syndrome with suggestion of linkage to chromosome 2

Epilepsy syndromes, defmed as clusters of symptoms or signs occurring consistently together, form the basis of the currently accepted classification of the epilepsies. The concept of epilepsy syndrome is practical for the diagnosis, prognosis, orientation of treatment, and selection of appropriate investigations, but it is of variable specificity and usually does not give information on causes and mechanisms of an epilepsy. Some syndromes, such as childhood absences, partial epilepsy with centrotemporal spikes, or juvenile myoclonic epilepsy, are precisely characterized while other syndromes such as grand mal on awakening or the multiple syndromes with myoclonic seizures are poorly delineated. The usefulness of the concept is limited to well-defined and generally accepted syndromes and many cases of epilepsy do not fit easily in recognizable syndromes. (J Child Neurol 1994; 9(Suppl):2S14-2S18).

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A NEURAL NETWORK-BASED CLASSIFICATION MODEL FOR PARTIAL EPILEPSY BY EEG SIGNALS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001408006594 ◽

2008 ◽

Vol 22 (05) ◽

pp. 973-985 ◽

Cited By ~ 4

Author(s):

CENK SAHIN ◽

SEYFETTIN NOYAN OGULATA ◽

KEZBAN ASLAN ◽

HACER BOZDEMIR ◽

RIZVAN EROL

Keyword(s):

Neural Network ◽

Cortical Excitability ◽

Correct Diagnosis ◽

Decision Support Tool ◽

Medical Problem ◽

Partial Epilepsy ◽

Classification Model ◽

Epilepsy Syndrome ◽

Eeg Signals ◽

Support Tool

Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient's epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to evaluate epileptic patients and classify subgroups of partial epilepsy by Multilayer Perceptron Neural Networks (MLPNNs). This is the first study to classify the partial epilepsy groups using the neural network according to EEG signals. 418 patients with epilepsy diagnoses according to International League against Epilepsy (ILAE, 1981) were included in this study. The epilepsy outpatients at the Neurology Department Clinic of Cukurova University Medical School between the years of 2002–2005 were examined and included in the study. The MLPNNs were trained by the parameters obtained from the EEG signals and clinical findings of the patients. Test results show that the MLPNN model is able to classify partial epilepsy with an accuracy of 91.5%. Moreover, new MLPNNs were constructed for determining significant variables on classification. The loss of consciousness in the course of seizure time variable caused the largest decrease in the classification accuracy when it was left out. In conclusion, we think that the classification performance of MLPNN model for partial epilepsy is satisfactory and this model may be used in clinical studies as a decision support tool to determine the partial epilepsy classification of the patients.

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New autosomal-dominant partial epilepsy syndrome

Pediatric Neurology ◽

10.1016/0887-8994(94)90184-8 ◽

1994 ◽

Vol 11 (2) ◽

pp. 95 ◽

Cited By ~ 6

Author(s):

Ingrid E. Scheffer ◽

Ian J. Hopkins ◽

A.Simon Harvey ◽

Samuel F. Berkovic

Keyword(s):

Autosomal Dominant ◽

Partial Epilepsy ◽

Epilepsy Syndrome

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Atypical benign partial epilepsy and a new variant of SLC35A3 gene plus 2p25.1 duplication. Phenotypic-Genotypic correlation?

10.21203/rs.3.rs-315555/v1 ◽

2021 ◽

Author(s):

Enrico Parano ◽

Piero Pavone ◽

Xena Giada Pappalardo ◽

Roberto Caraballo ◽

Andrea Domenico Praticò ◽

...

Keyword(s):

Status Epilepticus ◽

Genetic Diagnosis ◽

Partial Epilepsy ◽

Heterozygous Mutation ◽

Chromosome 2 ◽

Rolandic Epilepsy ◽

Behavioral Impairment ◽

Epileptiform Discharges ◽

New Variant ◽

Eeg Pattern

Abstract Background Atypical Benign Partial Epilepsy (ABPE), recognized also as pseudo-Lennox syndrome, is an uncommon form of epilepsy characterized by generalized minor seizures such as atonic, absences, or myoclonic seizures, and electroencephalographic pattern of focal or multifocal sharp waves with activation of epileptiform discharges during sleep. ABPE is indicated as a variant of ESES (ILAE classification 2017). ESES is a clinical entity that is characterized by encephalopathy with cognitive/ behavioral regression and EEG pattern of electrical status epilepticus during slow sleep. Fine and gross motor, language and social/behavioral impairment are associated symptoms, which may have reversible or persistent course. ABPE has been ascribed to the group of the “epilepsy aphasia spectrum” disorders, which includes also Rolandic Epilepsy, Landau-Kleffner syndrome, and electrical status epilepticus during sleep/continuous spike-wave during sleep. We report a young boy with a previous mild motor and language delay, who at 2-year-old presented with recurrent atonic seizures and an EEG pattern consisting of continuous spike and waves during sleep. Methods Next Generation Sequencing and microarray technology were used to investigate the molecular background of the proband, and the findings were then analyzed and integrated with clinical data. Results The child has been followed up to 7 years of age showing a progressive, complete EEG resolution and a normalization of the previous motor and cognitive impairment in association to the levetiracetam treatment. Genetic diagnosis displayed a novel heterozygous mutation c.310G>A of the SLC35A3 gene and a partial duplication of the short arm of chromosome 2, which might have pathogenic correlation with the neurological signs presented by the child. Conclusions Data generated by a genomic approach disclose a more comprehensive view of the genotype-phenotype correlation analysis for the novel pathogenic variant and ABPE. The relationship between the phenotypic manifestations of the child and genetic data is discussed.

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