European Journal of Molecular & Clinical Medicine

: Introduction: Seizure disorder is a one of major health problem in adults mostly in late adulthood in which chances of seizures are increased especially due to comorbidities like cerebrovascular stroke, CNS infection, degenerative disease of brain, and brain tumor. So we study 50 adult patients to identify various etiology of seizures.
Materials and methods: We check for various parameters like complete blood count, blood sugar level, renal function tests with electrolytes, liver function tests, brain imaging and Electroencephalogram. Result: With the help of this study, we identify that most common cause for seizure is idiopathic in less than 50 years of age and post stroke epilepsy in more than 50 years of age. Generalised tonic clonic seizure is most common type of seizure. With the help of newer neuro-imaging modalities and EEG it is possible to find out specific etiology of seizure, so EEG and imaging study should be integral part of investigation work of patient with seizure disorder.
Conclusion: The present study was an effort to find out the various etiology, type of seizures in adults, clinical profile and response to antiepileptic drugs. Every patient should be investigated thoroughly and diagnosed and best suitable drug given depending upon type of seizures to the patient for proper control of seizures and also improve morbidity and mortality due to seizures.
 

Autism Spectrum Disorder (ASD) is a sort of developmental issue of the
nervous system, with center impedances in social connections, creative mind,
communications, adaptability of thought,intrigue andrestricted range of activities.
Examination of electroencephalographic (EEG) signals based on autism is explored in
this work. Even so, it is critical to identify autism by the analysis of the EEG signal.
Hence feature extraction based on the EEG signals takes part a prominent role in
autism recognition. A practical feature extraction technique variational mode
decomposition (VMD) to diagnose autism is narrated in this paper. Further, the features
extracted are fed to classifiers ANN, KNNand SVM to stratify autism.SVM classifier
shows a finer classification performance when compared to extant techniques