Search results

Epilepsy is a chronic neurological disorder that can have profound physical, social and psychological consequences. We aimed to assess the clinical predictors of quality of life of people with epilepsy. We recruited 31 patients suffering from epilepsy in this cross-sectional study. Their clinical profile was recorded. Quality Of Life in Epilepsy (QOLIE-31) was used to assess quality of life of our patients. Depression was screened by Neurological Disorders Depression Inventory in Epilepsy (NDDI-E). Among all the clinical variables, only seizure frequency significantly correlated with seizure worry (P=0.002), emotional well-being (P=0.026) and social functions (P=0.013) subscales of QOLIE-31. NDDIE score showed a significant negative correlation with all the subscales of QOLIE-31 except medication effects (P=0.993). A significant positive correlation was also noted between seizure frequency and NDDI-E score (r=0.417, P=0.020). Seizure frequency and depression are the most important predictors of quality of life in epilepsy patients. The management of patients with epilepsy should not only be aimed at just preventing seizures but the treating clinicians should also be cognizant about depression which itself can significantly affect the quality of life of patients.

This descriptive correlational study describes behavior control by executive function (EF) and explores the relationship among age at seizure onset, duration of epilepsy, seizure frequency, number of antiepileptic drugs (AEDs), family income, the caregiver's education, home environment and behavior control by EF in preschool children with epilepsy.

The purposive sample was 69 caregivers of preschool children with epilepsy. Data were collected in two medical centers in Bangkok from June 2019 to February 2020. The research instruments constituted: (1) a sociodemographic and medical information form for children with epilepsy and the caregiver; (2) early childhood-home observation for the measurement of the environment (EC-HOME) inventory and (3) the behavior rating inventory of executive function-preschool version® (BRIEF-P). The data were analyzed using Pearson's product-moment correlation and Spearman's Rho correlation.

Most of the participants had quite high scores on home environment (mean = 44.35) and mildly elevated levels of EF deficit (mean = 61.04). The duration of epilepsy and the number of AEDs were positively correlated with behavior control by EF. Family income was negatively associated with behavior control by EF. However, age at seizure onset, seizure frequency, the caregiver's education and home environment had no association with behavior control by EF.

Preschool children with epilepsy have poor behavioral control by EF. Consequently, healthcare providers should promote interventions in children to control seizures and to decrease the factors that impact the development of EF.

Historically, epilepsy was attributed to non‐medical causes such as demonic possession, a gift from God, witchcraft, and mental illness. Only with the advent of the electroencephalogram (EEG) in the 1930s did the medical profession begin to document the neurological basis for the condition. Now a wide range of anticonvulsants allow most epileptics to maintain partial or total control over their seizures. Nevertheless, many epileptics routinely face discouraging social limitations, such as difficulty obtaining a driver's license, employment discrimination, problems with dating and marriage, restrictions on sports and activities, and the expense of medication.

The purpose of this paper is to review the application of a syndromic approach to seizure disorders in intellectual disabilities, in the light of recent advances in research and the International League Against Epilepsy (ILAE) Report on classification in 2010.

The ILAE Report is reviewed with an emphasis on neurodevelopmental seizure disorders, which may present to clinicians working in the field of adult intellectual disability. The advantages of applying a syndromic approach and the difficulties often encountered are also discussed.

Adopting a syndromic approach to seizure disorders in adults with intellectual disability should lead to rational prescribing, appropriate packages of care, and an improvement in the quality of research in this field.

This paper highlights the importance of identifying epilepsy syndromes in adults with intellectual disability, in the light of recent international reports on classification. It is of value to clinicians (particularly psychiatrists and learning disability nurses) practising in the field of epilepsy and intellectual disability.

This paper examines, by reference to a telecare service evaluation in the Republic of Ireland, the role and potential of bed epilepsy sensors. It points to benefits for both users and carers that arise from the sensors both enabling speedy responses in the event of a seizure, and in their providing reassurance and a better quality of life for both parties.

The purpose of this paper is to ascertain the training status of staff in care homes for people with intellectual disabilities managed by the non‐health sector in High Wycombe, with regard to administering emergency antiepileptic medication and to identify training needs.

Management of seizures in people with intellectual disabilities and epilepsy comes with its challenges. Although there are clear guidelines for the same, implementing them in the community, especially in the non‐health sector setting, raises the issue of training staff in the administration of emergency antiepileptic medication. A survey was undertaken in response to the varying staff training needs. A short semi‐structured questionnaire was designed and administered over the telephone to the managers of these care homes.

Less than half of the care homes had staff trained to administer emergency antiepileptic medication. The commonest reason cited was their policy of admitting only patients with well controlled seizures. This paper identified the need for staff training and raising awareness amongst relevant healthcare professionals.

The survey highlights the practical difficulties arising in the implementation of national and trust level healthcare policies in the community.

This paper is of value to clinicians working with people with intellectual disabilities and epilepsy, GPs, and staff and management in the care homes in the health and non‐health sectors. It raises questions around delineation of responsibility and communication between various professionals in various levels of care involved in managing people with intellectual disabilities and epilepsy to ensure provision of safe and effective care for this population.

Defines epilepsy as a medical condition and discusses its significance in the work environment; some jobs are precluded; common barriers and misconceptions need to be overcome; the employee with epilepsy may need counselling; if possible other employees should be given positive information about the condition and its control.

This work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.

In this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.

For the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.

Epilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.

A district‐wide audit was completed for five different aspects of the care of patients with epilepsy: principally issues for women of child‐bearing age and the offer of annual reviews to patients, possibly conducted by questionnaire. There were improvements in care but less than half the original participants completed the audit, perhaps because it was over‐complicated. Review by questionnaire seemed acceptable to many patients. The authors believe this is the largest audit cycle of this subject in general practice. Although the numbers of people with epilepsy is not large, the care of these patients is easily overlooked. The project has several lessons for all those introducing and developing clinical governance.