International Conference on Epilepsy & Treatment September 21-22, 2015 Baltimore, USA

E Viteva studied at Medical University–Plovdiv, Department of Neurology. She is a member of the Bulgarian Neurological Society, the Bulgarian Association of neurosonology, the Bulgarian society of Child Neurology, Psychiatry and Psychology of Development.

Introduction: The rate of seizure recurrence is an important aspect of short-term outcome in patients with status epilepticus (SE) which has received scarce attention by few studies.Purpose: Our purpose was to identify the risk factors for recurrent seizures during hospitalization of patients with SE and the role of seizure recurrence for functional outcome.Methods: We performed a prospective study of 95 consecutive patients diagnosed with SE and treated in a neuro-intensive care unit over a period of 3 years. Demographics and clinical data concerning established epilepsy and SE were collected and their relationship to the rate of seizure recurrence was analyzed. SE functional outcome assessment was based on the results from the Glasgow Outcome Scale. Results: There were single seizures in 26 (27.37) of our study participants after SE management, in 15 (15.79%) patients recurrent clusters of seizures or SE were observed. The rate of seizure recurrence was significantly higher in participants with longer duration of prior epilepsy and SE, SE poly therapy, recurrent SE, established epilepsy with polymorphic seizures, poor compliance and inadequate antiepileptic treatment. On multivariate regression analysis the predictive role of prior epilepsy duration and SE duration for seizure recurrence was confirmed P<0.001 (F=14.52). No correlation was found between the rate of seizure recurrence and functional outcome P>0.05. Conclusion: The study confirms the role of some clinical factors for the rate of seizure recurrence in hospitalized patients with SE and denies the direct association of seizure recurrence with functional outcome. Our results may be useful for finding more successful management strategies of the post-SE period.

Gavzandarounkola Hakimeh had completed her B.V.M at the age of 24 years from Shahid Chamran University and PhD of physiology at the age of 29 years from Tehran University Faculty of Veterinary. Her PhD thesis was “Evaluation of behavioral and electrophysiological effects of the unsaturated fatty acid DHA in experimental model of seizures. She performed her PhD thesis in Pasteur Institute of Iran, Tehran. She is teaching physiology in Amol University of special modern technology. She had 2 papers; “Effect of Echinacea purpurea on antibody production against fowl influenza “and “Synergistic effect of docosahexaenoic acid on anticonvulsant activity of valproic acid and lamotrigine in animal seizure model”. Also she had given several oral presentations and posters in some international conferences.

Introduction: Combination therapy is a common strategy to increase the efficacy and decrease side effects of antiepileptic drugs. Considerable anticonvulsant potential as well as appropriate safety of Docosahexaenoic acid (DHA) makes it a good candidate for combination therapy. In this study, we evaluated the influence of DHA on the anticonvulsant activity of one of the common antiepileptic drugs (AEDs), lamotrigine, in the kindling model of epilepsy in rats.rnrnMethods: Dose-antiseizure curve for lamotrigine (60 min after i.p. injection of doses 10, 12.5, 15, and 20 mg/kg to rats) against complex partial seizures induced by amygdala-rapid kindling was obtained. DHA was injected at doses 1, 300 and 1000μM in kindling model and DHA anti-seizure effect examined in this model, 15 min after intracerebroventricular (i.c.v.) injection.. Then a non-protective dose of DHA 300μM (ED25) was added to increasing doses of lamotrigine and new dose-antiseizure curve was obtained.Results and conclusion: DHA showed dose-dependent protective effect against kindled seizure (1087.824 μM). DHA at ED25 caused 4.938 fold increases in potency of Lamotrigine (ED50 value from 13.10 decreased to 2.65 mg/kg). Compusyn analysis indicated synergistic anticonvulsant interaction between DHA and Lamotrigine. As a result, Co-administration of the safer and inexpensive anticonvulsant agent DHA, with common AEDs can be considered as a good strategy in the treatment of epilepsy.

Manuela Stucchi is a Trained Clinical Psychologist. She graduated at University of Padova (Italy) in 2009 with highest final grade and will finish her training in psychodynamic psychotherapy in late 2015. She collaborates with the Child and Adolescent Neuropsychiatric Unit in Padova (Italy) till 2010 as a Clinical Psychologist. She has co-conducted projects with Gatta and her research group for alcohol prevention in school. She has published in reputed journals regarding her work with epilepsy and psychopathology and other projects. She presented her works in several national and international conferences.

Scientific literature shows high rates of psychopathology in paediatric patients with epilepsy: high prevalence of anxiety-depressive disorder, thought disorders, along with attention and social issues. Most studies suggest a common etiopathogenesis of epilepsy and psychopathology. Therefore, clinical assessment with these patients should take into account both psychological and neurological aspects. This research aims to find a relation between the onset of epilepsy and the development of any psychopathology in children accessing the Italian NHS after the first seizure. For 2 years 49 children (aged 4-18) with new-onset epilepsy, and their families underwent a semestral clinical assessment including clinical interview, dimensional and categorical questionnaires. The study analysed the demographic, psychosocial and epileptic profile, monitoring their natural evolution and involvement in a potential psychiatric co-morbidity. Consistently with the literature, we found a high prevalence of anxiety-depressive disorder and ADHD, both significantly associated with psychosocial, epilepsy-related, and quality of life variables. Although the results have shown a general improvement of the diagnosed psychopathology, it was also observed and enlargement of the psychopathological spectrum, and an impairment of skills after one year. After 18 months the 90% of patients reduced seizure frequency and duration, maintaining the psychiatric scores. The 43% was recommended to a specific psycho-diagnostic procedure, refused by 14%. Although agreeing with the hypothesis of a common neurobiological damage causing epilepsy and psychopathology, the role of psychological factors, environmental characteristics and stressful life events cannot be excluded. Therefore, a global care and a multidisciplinary diagnosis considering an eventual psychopathological profile are necessary.

Mohammad-Parsa Hosseini is a PhD candidate in the Department of Electrical and Computer Engineering at Rutgers University, NJ. He is contributing with Radiology Image Analysis Lab, Henry Ford Health System, MI, on medical imaging and data analysis projects. He has published many journal and conference papers in the field of epilepsy health care and medical data processing. He has been serving as a Reviewer in some engineering conferences.

Epilepsy is defined by the occurrence of at least two recurrent epileptic seizures in less than 24 hours. Neurologists and trained physicals visually scan the long term EEG records to detect epileptic seizures but it takes many hours for making a distinction between normal and abnormal activities. Therefore, developing an automatic method for detection and monitoring epileptic seizures in long-term EEG would greatly assist healthcare. Artificial intelligence programs have been developed to perform diagnosis and make recommendations for therapy. In this study an automatic method is proposed for seizure detection task. In the first step, EEG recording is preprocessed to eliminate non-cerebral origin noises and artifacts. Then, the rhythmic nature of seizure discharge is analyzed by using of a wavelet transform. After evaluating the result of the common wavelet transforms, Daubechies-4 has been selected to find approximation and detail of seizure segment. In the next step, many frequency and time domain features are extracted from seizure segment. Then, Independent Component Analysis (ICA) is used for feature reduction and generating uncorrelated and independent features from the data. Finally, among pattern recognition classifiers, support vector machine is developed to classify the extracted features to normal or epileptic. To avoid overfitting in classification, the ensemble learning method is applied for classification task. Using a clinical database on five epileptic patients and four normal subjects, a sensitivity of 84% is achieved by leave-one-out cross validation. The results confirm the validity of the proposed method for automatic seizure detection in long-term EEG records.