The Individual Ictal Fingerprint: Combining Movement Measures With Ultra Long-Term Subcutaneous EEG in People With Epilepsy

Background: Epileptic seizures are caused by abnormal brain wave hypersynchronization leading to a range of signs and symptoms. Tools for detecting seizures in everyday life typically focus on cardiac rhythm, electrodermal activity, or movement (EMG, accelerometry); however, these modalities are not very effective for non-motor seizures. Ultra long-term subcutaneous EEG-devices can detect the electrographic changes that do not depend on clinical changes. Nonetheless, this also means that it is not possible to assess whether a seizure is clinical or subclinical based on an EEG signal alone. Therefore, we combine EEG and movement-related modalities in this work. We focus on whether it is possible to define an individual “multimodal ictal fingerprint” which can be exploited in different epilepsy management purposes.Methods: This study used ultra long-term data from an outpatient monitoring trial of persons with temporal lobe epilepsy obtained with a subcutaneous EEG recording system. Subcutaneous EEG, an EMG estimate and chest-mounted accelerometry were extracted from four persons showing more than 10 well-defined electrographic seizures each. Numerous features were computed from all three modalities. Based on these, the Gini impurity measure of a Random Forest classifier was used to select the most discriminative features for the ictal fingerprint. A total of 74 electrographic seizures were analyzed.Results: The optimal individual ictal fingerprints included features extracted from all three tested modalities: an acceleration component; the power of the estimated EMG activity; and the relative power in the delta (0.5–4 Hz), low theta (4–6 Hz), and high theta (6–8 Hz) bands of the subcutaneous EEG. Multimodal ictal fingerprints were established for all persons, clustering seizures within persons, while separating seizures across persons.Conclusion: The existence of multimodal ictal fingerprints illustrates the benefits of combining multiple modalities such as EEG, EMG, and accelerometry in future epilepsy management. Multimodal ictal fingerprints could be used by doctors to get a better understanding of the individual seizure semiology of people with epilepsy. Furthermore, the multimodal ictal fingerprint gives a better understanding of how seizures manifest simultaneously in different modalities. A knowledge that could be used to improve seizure acknowledgment when reviewing EEG without video.

Suppression of Electrographic Seizures Is Associated with Amelioration of QTc Interval Prolongation in Patients with Traumatic Brain Injury

Introduction: Disorders in electroencephalography (EEG) are commonly noted in patients with traumatic brain injury (TBI) and may be associated with electrocardiographic disturbances. Electrographic seizures (ESz) are the most common features in these patients. This study aimed to explore the relationship between ESz and possible changes in QTc interval and spatial QRS-T angle both during ESz and after ESz resolution. Methods: Adult patients with TBI were studied. Surface 12-lead ECGs were recorded using a Cardiax device during ESz events and 15 min after their effective suppression using barbiturate infusion. The ESz events were diagnosed using Masimo Root or bispectral index (BIS) devices. Results: Of the 348 patients considered for possible inclusion, ESz were noted in 72, with ECG being recorded in 21. Prolonged QTc was noted during ESz but significantly ameliorated after ESz suppression (540.19 ± 60.68 ms vs. 478.67 ± 38.52 ms, p < 0.001). The spatial QRS-T angle was comparable during ESz and after treatment. Regional cerebral oximetry increased following ESz suppression (from 58.4% ± 6.2 to 60.5% ± 4.2 (p < 0.01) and from 58.2% ± 7.2 to 60.8% ± 4.8 (p < 0.05) in the left and right hemispheres, respectively). Conclusion: QTc interval prolongation occurs during ESz events in TBI patients but both it and regional cerebral oximetry are improved after suppression of seizures.

Case Report: PAFAH1B1 Mutation and Posterior Band Heterotopia With Focal Temporal Lobe Epilepsy Treated by Responsive Neurostimulation

Subcortical band heterotopia (SBH), also known as double cortex syndrome, is a malformation of cortical development caused by inherited or somatic gene variants. We present a case of a young adult with posterior SBH and electroclinical features of focal neocortical temporal lobe epilepsy. Genomic blood analysis identified a pathogenic somatic mosaicism duplication variant of the PAFAH1B1 gene. Despite bilateral cortical MRI abnormalities, the interictal and ictal EEG findings indicated a focal epileptogenic region in the left posterior temporal region. Chronic responsive cortical neurostimulation across two four-contact depth electrodes placed 5 mm on either side of the maximal interictal spiking identified during intraoperative electrocorticography resulted in a consistent 28% reduction in duration of electrographic seizures and as well as constricted propagation. Although electrographic seizures continued, the family reported no clinical seizures and a marked improvement in resistant behaviors. This observation supports that focal neocortical neuromodulation can control clinical seizures of consistently localized origin despite genetic etiology, bilateral structural brain abnormalities, and continuation of non-propagating electrographic seizures. We propose that a secondary somatic mutation may be the cause of the focal neocortical temporal lobe epilepsy.

Improving consistency and accuracy of neonatal amplitude-integrated electroencephalography

Objective: To improve the utilization of amplitude-integrated electroencephalography (aEEG) in a neonatal unit by improving aEEG documentation, aEEG knowledge and pattern recognition ability of neonatal staff. Methods: A quality improvement (QI) program comprising two plan-do-study-act (PDSA) cycles was conducted in a level 3 neonatal intensive care unit. The first cycle was focused on improving aEEG documentation with the primary outcome indicator being compliance with aEEG documentation. The second cycle was focused on aEEG interpretation in a healthcare professional education program with the outcome indicators being accuracy of seizure identification on aEEG and change in conventional EEGs (EEG) performed. Other outcome indicators included accuracy in identification of background pattern, sleep-wake cycles and artefacts. Process indicators included improvement in aEEG-related knowledge. Interventions: First PDSA cycle – lectures on aEEG interpretation, a bedside key and documentation form. Second PDSA cycle – online aEEG education pack, detailed aEEG guideline. Results: There was a significant improvement in aEEG documentation after the implementation of both PDSA cycles. 7 of the 46 patients (15.2%) had isolated electrographic seizures which would not have been identified in the pre-aEEG monitoring era. There was an increase in the number of patients with EEGs done, but a steady decrease in number of EEGs per patient. Conclusions: With the successful application of standardized QI methods, improvements in outcome indicators such as correct aEEG pattern recognition and improved coverage of at risk infants with EEGs were observed. Our QI measures were associated with improvement in aEEG pattern recognition.

Wearable Reduced-Channel EEG System for Remote Seizure Monitoring

Epitel has developed Epilog, a miniature, wireless, wearable electroencephalography (EEG) sensor. Four Epilog sensors are combined as part of Epitel's Remote EEG Monitoring platform (REMI) to create 10 channels of EEG for remote patient monitoring. REMI is designed to provide comprehensive spatial EEG recordings that can be administered by non-specialized medical personnel in any medical center. The purpose of this study was to determine how accurate epileptologists are at remotely reviewing Epilog sensor EEG in the 10-channel “REMI montage,” with and without seizure detection support software. Three board certified epileptologists reviewed the REMI montage from 20 subjects who wore four Epilog sensors for up to 5 days alongside traditional video-EEG in the EMU, 10 of whom experienced a total of 24 focal-onset electrographic seizures and 10 of whom experienced no seizures or epileptiform activity. Epileptologists randomly reviewed the same datasets with and without clinical decision support annotations from an automated seizure detection algorithm tuned to be highly sensitive. Blinded consensus review of unannotated Epilog EEG in the REMI montage detected people who were experiencing electrographic seizure activity with 90% sensitivity and 90% specificity. Consensus detection of individual focal onset seizures resulted in a mean sensitivity of 61%, precision of 80%, and false detection rate (FDR) of 0.002 false positives per hour (FP/h) of data. With algorithm seizure detection annotations, the consensus review mean sensitivity improved to 68% with a slight increase in FDR (0.005 FP/h). As seizure detection software, the automated algorithm detected people who were experiencing electrographic seizure activity with 100% sensitivity and 70% specificity, and detected individual focal onset seizures with a mean sensitivity of 90% and mean false alarm rate of 0.087 FP/h. This is the first study showing epileptologists' ability to blindly review EEG from four Epilog sensors in the REMI montage, and the results demonstrate the clinical potential to accurately identify patients experiencing electrographic seizures. Additionally, the automated algorithm shows promise as clinical decision support software to detect discrete electrographic seizures in individual records as accurately as FDA-cleared predicates.

EEG Abnormalities and Their Radiographic Correlates in a COVID-19 Inpatient Cohort

ABSTRACT:Objective:To identify the prevalence of EEG abnormalities in patients with COVID-19 with neurologic changes, their associated neuroimaging abnormalities and rates of mortality.Methods:A retrospective case series of 192 adult COVID-19 positive inpatients with EEG performed between March and June 2020 at 4 hospitals: 161 undergoing continuous, 24 routine, and 7 reduced- montage EEG. Study indication, epilepsy history, intubation status, administration of sedatives or antiseizure medications, metabolic abnormalities, neuroimaging pathology associated with epileptiform abnormalities, and in-hospital mortality were analyzed.Results:EEG indications included encephalopathy (54.7%), seizure (18.2%), coma (17.2%), focal deficit (5.2%), and abnormal movements (4.6%). Epileptiform abnormalities occurred in 39.6% of patients: focal intermittent epileptiform discharges in 25.0%, lateralized periodic discharges in 6.3%, and generalized periodic discharges in 19.3%. Seizures were recorded in 8 patients, 3 with status epilepticus. Antiseizure medication administration, epilepsy history, and older age were associated with epileptiform abnormalities. Only 26.3% of patients with any epileptiform abnormality, 37.5% with electrographic seizures, and 25.7% patients with clinical seizures had known epilepsy. Background findings included generalized slowing (88.5%), focal slowing (15.6%), burst suppression (3.6%), attenuation (3.1%), and normal EEG (3.1%). Neuroimaging pathology was identified in 67.1% of patients with epileptiform abnormalities, over two-thirds acute. In-hospital mortality was 39.5% for patients with epileptiform abnormalities, 36.2% for those without. Risk factors for mortality were coma and ventilator support at time of EEG.Significance:This article highlights the range of EEG abnormalities frequently associated with acute neuroimaging abnormalities in COVID-19. Mortality rates were high, particularly for patients in coma requiring mechanical ventilation. These findings may guide the prognosis and management of patients with COVID-19 and neurologic changes.

Quinine in the Treatment of Malignant Migrating Partial Seizures in Infancy: A Case Report

The syndrome of malignant migrating partial seizures in infancy was first described by Coppola and colleagues in 1995. The International League Against Epilepsy defines this form of epilepsy as a seizure onset in the first 6 months of life, occurrence of almost continuous migrating polymorphous focal seizures, combined with multifocal ictal EEG discharges, and progressive deterioration of psychomotor development. Most cases are pharmacoresistant and have poor outcomes. A lot of publications described the trial of several medications such as Stiripentol, Rufinamide, Cannabidiol, and finally Ketogenic diet, to control the refractory devastating seizures. We describe a 13-month-old girl with malignant migrating partial seizures in infancy who was started on Quinine for the control of her refractory seizures after the trial of multiple antiepileptic medications that failed to control her seizures, including Clonazepam, Carbamazepine, Phenobarbitone, Phyntion, Midazolam, Valproate, Perampanel & Ketogenic diet, all were tried by different combination at different times. Finally, as malignant migrating partial seizures in infancy are sometimes linked to K channelopathy, a trial of Quinine was given in a dose of 30mg/kg/d. Patients showed an excellent response with control of clinical & electrographic seizures. Now she is seizure-free for five months and undergoing physiotherapy. She started rolling over but doesn't have much improvement in motor milestones, is not following or cooing, and is unable to say clear words. Keywords: MMPSI – malignant migrating partial seizures in infancy- Quinine – Intractable epilepsy- CPLANE-1 gene defect

Seizure Forecasting Using Long-Term Electroencephalography and Electrocardiogram Data

Electroencephalography (EEG) has been used to forecast seizures with varying success. There is an increasing interest to use electrocardiogram (ECG) to help with seizure forecasting. The neural and cardiovascular systems may exhibit critical slowing, which is measured by an increase in variance and autocorrelation of the system, when change from a normal state to an ictal state. To forecast seizures, the variance and autocorrelation of long-term continuous EEG and ECG data from 16 patients were used for analysis. The average period of recordings was 161.9 h, with an average of 9 electrographic seizures in an individual patient. The relationship between seizure onset times and phases of variance and autocorrelation in EEG and ECG data was investigated. The results of forecasting models using critical slowing features, seizure circadian features, and combined critical slowing and circadian features were compared using the receiver-operating characteristic curve. The results demonstrated that the best forecaster was patient-specific and the average area under the curve (AUC) of the best forecaster across patients was 0.68. In 50% of patients, circadian forecasters had the best performance. Critical slowing forecaster performed best in 19% of patients. Combined forecaster achieved the best performance in 31% of patients. The results of this study may help to advance the field of seizure forecasting and lead to the improved quality of life of people who suffer from epilepsy.