METHODS: A cross-sectional study was carried out where routine EEG assessment was performed in 206 consecutive acute stroke patients without seizures. The demographic data and clinical stroke assessment were collated using the National Institutes of Health Stroke Scale (NIHSS) score with neuroimaging. Associations between EEG abnormalities and clinical features, stroke characteristics, and NIHSS scores were evaluated.
RESULTS: The mean age of the study population was 64.32 ± 12 years old, with 57.28% consisting of men. The median NIHSS score on admission was 6 (IQR 3-13). EEG was abnormal in more than half of the patients (106, 51.5%), which consisted of focal slowing (58, 28.2%) followed by generalized slowing (39, 18.9%) and epileptiform changes (9, 4.4%). NIHSS score was significantly associated with focal slowing (13 vs. 5, p
Objective: We aimed to study the prevalence of visual memory dysfunction among epilepsy patients and identify the predictors that could contribute to the impairment.
Materials and Methods: This was a cross-sectional study. We analyzed 250 patients with epilepsy from neurology clinic at our tertiary center. Assessment of visual memory was done using Wechsler Memory Scale-IV (WMS-IV) with scores from subsets of visual reproduction I, II and designs I, II contributing to visual memory index (VMI) score. The correlation between continuous variables was analyzed using Pearson correlation; whereas the VMI scores of different factors were analyzed via a 1-way ANOVA test. The statistical significance was set at P < 0.05.
Results: The prevalence of visual memory dysfunction in our epilepsy population was 37.2%. Analysis of individual predictors showed that older patients, lower educational level, combined generalized and focal types of epilepsy, longer duration of epilepsy, greater number of antiepileptic drugs (AEDs) used, and abnormal neuroimaging contributed to poor visual memory. Multiple logistic regression analysis showed that educational level, types of epilepsy, and the number of AEDs used were significant predictors for visual memory impairment.
Conclusion: Visual memory dysfunction in patients with epilepsy was due to manifold confounding factors. Our findings enabled us to identify patients with visual memory dysfunction and modifiable factors that contribute to it. WMS-IV is a suitable assessment tool to determine visual memory function, which can help clinicians to optimize the patients' treatment.
METHODS: A cross-sectional study on 284 epilepsy patients was performed in a local tertiary centre. The demographic and clinical epilepsy data were collected. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) questionnaires were utilised to determine the quality of life and daytime hypersomnolence of epilepsy patients, respectively.
RESULTS: Poor sleep quality was reported in 78 (27.5%) patients while daytime hypersomnolence was present in 17 (6%) patients. The predictors of poor sleep quality include structural causes (OR = 2.749; 95% CI: 1.436, 5.264, p = 0.002), generalised seizures (OR = 1.959, 95% CI: 1.04, 3.689, p = 0.037), and antiseizure medications such as Carbamazepine (OR = 2.34; 95% CI: 1.095, 5.001, p = 0.028) and Topiramate (OR 2.487; 95% CI: 1.028, 6.014, p = 0.043). Females are 3.797 times more likely score higher in ESS assessment (OR 3.797; 95% CI: 1.064, 13.555 p = 0.04).
DISCUSSION: Sleep disturbances frequently coexist with epilepsy. Patients should be actively evaluated using the PSQI and ESS questionnaires. It is imperative to identify the key factors that lead to reduced sleep quality and heightened daytime sleepiness in patients with epilepsy, as this is essential to properly manage their condition.
METHOD: This study included a total of 44 participants without subjective olfactory disturbances. Lavender and normal saline were used as the olfactory stimulant and control. Electroencephalogram was recorded and power spectra were analysed by the spectral analysis for each alpha, beta, delta, theta and gamma bandwidth frequency upon exposure to lavender and normal saline independently.
RESULTS: The oscillatory brain activities in response to the olfactory stimulant indicated that the lavender smell decreased the beta activity in the left frontal (F7 electrode) and central region (C3 electrode) with a reduction in the gamma activity in the right parietal region (P4 electrode) (p < 0.05).
CONCLUSION: Olfactory stimulants result in changes of electrical brain activities in different brain regions, as evidenced by the topographical brain map and spectra analysis of each brain wave.
Method: Potential studies were identified through a systematic search of Scopus, Science Direct, Google Scholar, and PubMed. The keywords used to identify relevant articles were "adherence," "AED," "epilepsy," "non-adherence," and "complementary and alternative medicine." An article was included in the review if the study met the following criteria: 1) conducted in epilepsy patients, 2) conducted in patients aged 18 years and above, 3) conducted in patients prescribed AEDs, and 4) patients' adherence to AEDs.
Results: A total of 3,330 studies were identified and 30 were included in the final analysis. The review found that the AED non-adherence rate reported in the studies was between 25% and 66%. The percentage of CAM use was found to be between 7.5% and 73.3%. The most common reason for inadequate AED therapy and higher dependence on CAM was the patients' belief that epilepsy had a spiritual or psychological cause, rather than primarily being a disease of the brain. Other factors for AED non-adherence were forgetfulness, specific beliefs about medications, depression, uncontrolled recent seizures, and frequent medication dosage.
Conclusion: The review found a high prevalence of CAM use and non-adherence to AEDs among epilepsy patients. However, a limited number of studies have investigated the association between CAM usage and AED adherence. Future studies may wish to explore the influence of CAM use on AED medication adherence.