Prevalence and predictors of seizure in patients with Alzheimer’s disease at a tertiary care center in Riyadh, Saudi Arabia

Purpose: To assess the prevalence and predictors of seizures in patients with Alzheimer’s disease (AD) at a Saudi tertiary hospital.Methods: A retrospective, matched case-control study was conducted using the electronic medical records of patients with AD who had an unprovoked seizure, from October 2015 to May 2018.Results: Nineteen cases and 195 controls were identified. Statistically significant risk factors for an unprovoked seizure in patients diagnosed with AD were hypertension (p = 0.001), autoimmune disease, stroke and TIA (p = 0.001). The multivariate logistic regression analysis identified hypertension (OR = 2.89; p = 0.009) and autoimmune disease (OR = 19.6; p = 0.045) as predictors of unprovoked seizure in AD patients.Conclusion: The occurrence of unprovoked seizures is more likely in severe cases of AD. In addition, the risk of seizure in patients with AD increases with two co-morbid conditions, hypertension, and autoimmune disease. However, further studies are required to determine the underlying mechanism of the association between the two risk factors and AD.

Electroencephalography Patterns in Children with First Unprovoked Seizure

Background First unprovoked seizure (FUS) is a neurological health problem that occurs in an estimated 2% of children aged 16 years or younger. Electroencephalography (EEG) is an electrophysiological technique to record electrical activities arising from the brain; this technique can be used to evaluate patients with suspected seizures, epilepsy, and unusual concomitants. The objective of this study is to describe the EEG patterns in children with FUS and the factors associated with these EEG results. Method A retrospective analytic study was conducted in the Neuropaediatric Clinic, Dr Soetomo General Academic Hospital. The medical record data were obtained from January 2018 to December 2019. Children aged one month to 18 years with FUS and their complete EEG records were included. Descriptive statistics and the chi-square test with Cochran's Q test and Mantel–Haenszel tests were used for statistical calculations. Results One hundred participants enrolled the study. The majority (54%) showed abnormal EEG, which was dominated by epileptiform discharges (68.5%) consisting of benign epileptiform with centro-temporal spikes (BECTS), focal and generalized sharp waves, focal and generalized spikes, and EEG seizures. Factors associated with abnormal EEG results were children aged ≥ 5 years (p = 0.07, OR = 3.093, 95% CI = 1.361–7.030), focal seizure type (p = 0.021, OR = 6.286, 95% CI = 1.327–29,779), and long seizure duration ≥ 5 minutes (p < 0.001, OR = 8.333, 95% CI = 3.029–22.929). Conclusion Children with abnormal EEG were at risk for recurrent seizures. Over 50% of children with FUS had abnormal EEG results. In the present study, abnormal EEG results were frequently found in children with FUS, especially in older children (≥ 5 years old), those with focal seizures, and those with long seizure durations (≥ 5 minutes).

Seizure in children: How important is EEG & Neuroimaging?

Seizure in children are generally indicating a potentially serious underlying systemic or CNS disorders that require thorough clinical examination, investigation and management. It is therefore important to establish accurate diagnosis of seizure and its etiology to manage such patients appropriately. We carried out this study to evaluate different etiology of seizures and its correlation with abnormal EEG & abnormal neuroimaging in the age group of 2mo to 14 years. 200 children presented with seizure to our department from January 2019 to November 2020 were enrolled in this prospective hospital-based study. Detailed history, clinical examination, investigation with special emphasis to EEG & neuroimaging was done and different correlation was drawn by using SSPS 18.0 statistical analysis. Among 200 cases, 6 to 10yr. age group constituted maximum (49%) number of cases. Male to female ratio is 1.5:1. GTCS is the predominant pattern of seizure (60%) in all age groups. EEG abnormality is found in 45%, mostly in focal seizure type. Neuroimaging abnormality found in 29%. Maximum cases (30%) had infectious etiology. Childhood seizure needs detailed history taking and careful examination. Vedio recording shown by parents / caregivers really help towards differentiating seizure from seizure mimics. EEG has a role in specific seizure type; Neuroimaging at times helps in diagnosis. There are few studies that describe neuroimaging [Computed Tomography (CT) and Magnetic Resonance Imaging (MRI)] and Electroencephalogram (EEG) data in children who present with new-onset seizures. The EEG is recommended as a part of the neurodiagnostic evaluation of the child with an apparent first unprovoked seizure.

Etiological classification of seizures in pediatric age group (6-12years) – MRI study

Background: A seizure is an occurrence of signs or symptoms due to abnormal excessive orsynchronous neuronal activity in the brain. The present study aims to study the etiological factorsand clinical profile for new-onset seizures in children aged 6-12 years and to determine thefrequency of Magnetic resonance imaging (MRI) abnormalities in the pediatrics age group with new-onset unprovoked seizure and those with inadequately investigated longstanding epilepsy andclassify the etiology based on the MRI findings. Methods: A prospective study involving a total of 50patients was recruited aged between 6 to 12 years. All of them underwent neuro-imaging with MRI.Uncooperative patients were imaged following sedation and monitoring by the anesthetist. Allchildren aged 6-12 years who presented with new-onset seizures were included. All MR images wereobtained at a 3-mm section thickness except magnetization-prepared rapid gradient-echo images,which are obtained at a 1.8-mm section thickness. Results: Of the 50 patients 28 presented withgeneralized tonic-clonic seizures, 12 with simple partial seizures, 10 with complex partial seizures.Generalized seizures were a more common presentation than partial seizures in children 6-12 yearsof age. Conclusion: With the positivity of the MRI in the new-onset seizure in children between 6-12 years in our study gives an important aspect of the essential factor of imaging in pediatric new-onset seizures.

High-Frequency Oscillations on Interictal Epileptiform Discharges in Routinely Acquired Scalp EEG: Can It Be Used as a Prognostic Marker?

IntroductionDespite all the efforts for optimizing epilepsy management in children over the past decades, there is no clear consensus regarding whether to treat or not to treat epileptiform discharges (EDs) after a first unprovoked seizure or the optimal duration of therapy with anti-seizure medication (ASM). It is therefore highly needed to find markers on scalp electroencephalogram (EEG) that can help identify pathological EEG discharges that require treatment.Aim of the studyThis retrospective study aimed to identify whether the coexistence of ripples/high-frequency oscillations (HFOs) with interictal EDs (IEDs) in routinely acquired scalp EEG is associated with a higher risk of seizure recurrence and could be used as a prognostic marker.Methods100 children presenting with new onset seizure to Children’s Medical Center- Dallas during 2015–2016, who were not on ASM and had focal EDs on an awake and sleep EEG recorded with sample frequency of 500 HZ, were randomly identified by database review. EEGs were analyzed blinded to the data of the patients. HFOs were visually identified using review parameters including expanded time base and adjusted filter settings.ResultsThe average age of patients was 6.3 years (±4.35 SD). HFOs were visually identified in 19% of the studied patients with an inter-rater reliability of 99% for HFO negative discharges and 78% agreement for identification of HFOs. HFOs were identified more often in the younger age group; however, they were identified in 11% of patients &gt;5 years old. They were more frequently associated with spikes than with sharp waves and more often with higher amplitude EDs. Patients with HFOs were more likely to have a recurrence of seizures in the year after the first seizure (P &lt; 0.05) and to continue to have seizures after 2 years (P &lt; 0.0001). There was no statistically significant difference between the two groups with regards to continuing ASM after 2 years.ConclusionIncluding analysis for HFOs in routine EEG interpretation may increase the yield of the study and help guide the decision to either start or discontinue ASM. In the future, this may also help to identify pathological discharges with deleterious effects on the growing brain and set a new target for the management of epilepsy.

A multimodal AI system for out-of-distribution generalization of seizure detection

Epilepsy is one of the most common severe neurological disorders worldwide. The International League Against Epilepsy (ILAE) define epilepsy as a brain disorder that generates (1) two unprovoked seizures more than 24 hrs apart, or (2) one unprovoked seizure with at least 60% risk of recurrence over the next ten years. Complete remission has been defined as ten years seizure free with the last five years medication free. This requires a cost-effective ambulatory ultra-long term out-patient monitoring solution. The common practice of self-reporting is inaccurate. Applying artificial intelligence (AI) to scalp electroencephalogram (EEG) interpretation is becoming increasingly common, but other data modalities such as electrocardiograms (ECGs) are simpler to collect and often recorded simultaneously with EEG. Both recordings contain biomarkers in the detection of seizures. Here, we propose a state-of-the-art performing AI system that combines EEG and ECG for seizure detection, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific inference-only tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 patients shortlisted by neurologists and 30 randomly selected). Across all datasets, our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for prior state-of-the-art approaches using EEG and ECG alone, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets can improve the accuracy and efficiency of epilepsy diagnoses.

Predictors of Recurrence after a First Unprovoked Seizure in Childhood: A Prospective Study

The second part of the revised definition of epilepsy by ILAE in 2014 allows a condition to be considered epilepsy after one seizure if there is a high risk of having another seizure; if the risk factor is not precisely be known we have to wait for another seizure. This definition necessitates search for probable risk factors. We aimed this study to assess the recurrence rate and associated risk factors for recurrences after a first unprovoked seizure in children within two years of first attack. This prospective study was conducted on in Banglabandhu Sheikh Mujib Medical University (BSMMU) from June 2016 to December 2018. Among 137 children finally 120 children aged between1 month to 14 years after a first seizure were followed up for 2 years. Diagnosis of seizure was confirmed on the basis of diagnostic criteria and none of the children was treated by any antiepileptic drugs after first episode. Overall recurrence rate within 2 years of follow up was 38%. Majority of recurrence (65%) observed within 6-10 months of initial seizure. Significant risk factors were an abnormal EEG finding (p=<0.001), focal seizure (p=<0.001), seizure at sleep (p=0.001) and initial presentation with status epilepticus (p=0.001). Abnormal neuroimage findings were also associated with seizure recurrence, but it was not statistically significant. Age of the patients and underlying motor and cognitive delay was not a significant risk factor for recurrence. A great percentage of first seizure didn’t show recurrence but there are so many factors can determine the possibilities of recurrence, early identification of risk factors specially the focal pattern of seizure, seizure in sleep, status epilepticus and abnormal electrophysiology are the best predictive factors of recurrence, so identifying the high risk group of recurrence helps to initiate early antiepileptic drug and prevent further recurrence.