Introduction stroke is a cerebrovascular disease. Early reperfusion in neurovascular units can reduce its morbidity and mortality. Even when neurovascular units exist, patients usually arrive late in the emergency department. to the purpose of this study was to determine prehospital delay in patients with acute ischemic stroke and associated factors. Methods we conducted a retrospective cross-sectional study in the neurology department of the Fann University Hospital in Dakar from January 1 s t to June 30 th , 2020. We included patients younger than 80 years seen in the emergency unit for ischemic stroke. The median time to presentation was calculated based on the time of stroke onset and that of arrival at the hospital. Multivariate analysis was used to determine factors associated with prehospital delay. Results a total of 56 patients were enrolled, among whom 58.6% arrived at the hospital in less than 3 hours. Of them, 37.5% presented to a level 3 or 4 hospital first. Less than 34% of our patient presented to a level 2-3 hospital in less than 3 hours. Based on bi- and multivariate analysis, being married (OR = 7.2 [CI à 95%: 1.5 - 35.8]), being a female (OR = 5.5 [CI à 95%: 1.5 - 19.8]) and having stroke during week days (OR = 4.3 [CI à 95%: 1.3-13.9]) were associated with prehospital delay. Conclusion most of our patients arrived late at a level 2 or 3 hospital. Being a married woman increased the risk of late arrival. This study highlights the importance of improving awareness in order to increase the proportion of patients potentially eligible for revascularization.

Introduction: The diagnosis of epilepsy is mainly based on clinical features. Electroencephalography (EEG) is mainly essential on classifying epilepsy and epileptic syndromes. Our purpose is to demonstrate EEG has a role in setting the diagnosis of epilepsy. Method: We have studied the EEG of 775 patients of all age-groups registered in the EEGs laboratory of Fann Teaching hospital from July 1 st to November 15 th 2018. We included all patients not previously known with epilepsy. The EEGs conclusion were taken from the digital database. We have split them into two groups EEG abnormal versus EEG normal. The statistical analyzes were made by Epi Info 7.2.3.1. Results: The median age was 14 years old. The patient age range was from 38 days to 86 years. Patients with abnormal EEG were at 38% of the total. After univariate study were significantly associated with an abnormal EEG patients who came for focal onset crisis (74%) and those who came for spasm (84%). All with status epilepticus had abnormal EEG. Patients with generalized crises had an abnormal EEG in 60% but that was not significant. Diagnosis seek by pediatrician was significantly associated abnormal EEG. Conclusion: EEG is a very affordable test which plays a key role on the diagnosis of epilepsy. It sensitivity is closely linked with the experience of the ordering physician. Broad awareness of epilepsy among healthcare professionals and the community would be an important step in improving patient's management.