Long-term Electroencephalography Research Articles

Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset.

Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients. We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation. Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones. We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.

Risk Factors for Pressure Ulcer by Electrodes in Long-term Electroencephalography Monitoring

Background: Although electroencephalography is known as non-invasive, long-term electroencephalography monitoring has a significant risk of pressure ulcers caused by electrodes. We investigated the incidence and risk factors of pressure ulcers in long-term electroencephalography monitoring for more than 24 hours.Methods: All patients who underwent long-term electroencephalography monitoring for more than 24 hours from January 2020 to December 2021 were reviewed. Their medical records were reviewed to evaluate the risk factors for pressure ulcers by electroencephalography electrodes. All patients were divided into the presence (lesion positive) and absence (lesion negative) of pressure ulcers confirmed by a clinician.Results: A total of 541 patients were analyzed in this study. Their mean age was 56.0±21.5, and the average duration of the long-term electroencephalography monitoring was 118.0±68.6 hours. Pressure ulcers were identified in 49 patients (9.1%). The odds ratios of age and recording time were 1.032 (95% confidence interval [CI], 1.008-1.056, <i>p</i>=0.009) and 1.022 (95% CI, 1.015-1.029, <i>p</i><0.001), respectively. The odds ratio of pressure ulcer was 2.702 (95% CI, 1.461-4.999, <i>p</i>=0.002) in patients with comatose mentality (1 point for eye response of Glasgow coma scale).Conclusions: The pressure ulcers caused by electrodes in long-term electroencephalography monitoring can be preventable with due diligence in clinical settings. Risk factor control to prevent pressure ulcers due to electroencephalography electrodes is needed.

Abnormal gamma phase-amplitude coupling in the parahippocampal cortex is associated with network hyperexcitability in Alzheimer's disease.

While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (∼40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 ± 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 ± 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology.

Using a standalone ear-EEG device for focal-onset seizure detection

BackgroundSeizure detection is challenging outside the clinical environment due to the lack of comfortable, reliable, and practical long-term neurophysiological monitoring devices. We developed a novel, discreet, unobstructive in-ear sensing system that enables long-term electroencephalography (EEG) recording. This is the first study we are aware of that systematically compares the seizure detection utility of in-ear EEG with that of simultaneously recorded intracranial EEG. In addition, we present a similar comparison between simultaneously recorded in-ear EEG and scalp EEG.MethodsIn this foundational research, we conducted a clinical feasibility study and validated the ability of the ear-EEG system to capture focal-onset seizures against 1255 hrs of simultaneous ear-EEG data along with scalp or intracranial EEG in 20 patients with refractory focal epilepsy (11 with scalp EEG, 8 with intracranial EEG, and 1 with both).ResultsIn a blinded, independent review of the ear-EEG signals, two epileptologists were able to detect 86.4% of the seizures that were subsequently identified using the clinical gold standard EEG modalities, with a false detection rate of 0.1 per day, well below what has been reported for ambulatory monitoring. The few seizures not detected on the ear-EEG signals emanated from deep within the mesial temporal lobe or extra-temporally and remained very focal, without significant propagation. Following multiple sessions of recording for a median continuous wear time of 13 hrs, patients reported a high degree of tolerance for the device, with only minor adverse events reported by the scalp EEG cohort.ConclusionsThese preliminary results demonstrate the potential of using ear-EEG to enable routine collection of complementary, prolonged, and remote neurophysiological evidence, which may permit real-time detection of paroxysmal events such as seizures and epileptiform discharges. This study suggests that the ear-EEG device may assist clinicians in making an epilepsy diagnosis, assessing treatment efficacy, and optimizing medication titration.

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation

Background Implementing automated seizure detection in long-term electroencephalography (EEG) analysis enables the remote monitoring of patients with epilepsy, thereby improving their quality of life. Objective The objective of this study was to explore an mHealth (mobile health) solution by investigating the feasibility of smartphones for processing large EEG recordings for the remote monitoring of patients with epilepsy. Methods We developed a mobile app to automatically analyze and classify epileptic seizures using EEG. We used the cross-database model developed in our previous study, incorporating successive decomposition index and matrix determinant as features, adaptive median feature baseline correction for overcoming interdatabase feature variation, and postprocessing-based support vector machine for classification using 5 different EEG databases. The Sezect (Seizure Detect) Android app was built using the Chaquopy software development kit, which uses the Python language in Android Studio. Various durations of EEG signals were tested on different smartphones to check the feasibility of the Sezect app. Results We observed a sensitivity of 93.5%, a specificity of 97.5%, and a false detection rate of 1.5 per hour for EEG recordings using the Sezect app. The various mobile phones did not differ substantially in processing time, which indicates a range of phone models can be used for implementation. The computational time required to process real-time EEG data via smartphones and the classification results suggests that our mHealth app could be a valuable asset for monitoring patients with epilepsy. Conclusions Smartphones have multipurpose use in health care, offering tools that can improve the quality of patients’ lives.

Epileptic EEG patterns recognition through machine learning techniques and relevant time-frequency features.

The present study is designed to explore the process of epileptic patterns' automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via a selection of machine learning (ML) techniques. The primary motivation for conducting such a research lies mainly in the need to investigate the long-term electroencephalography (EEG) recordings' visual examination process, often considered as a time-consuming and potentially error-prone procedure, requiring a great deal of mental focus and highly experimented neurologists. On attempting to resolve such a challenge, a number of state-of-the-art ML algorithms have been evaluated and compare in terms of performance, to pinpoint the most effective algorithm fit for accurately extracting epileptic EEG patterns. Based on intracranial as well as simulated EEG data, the attained findings turn out to reveal that the randomforest (RF) method proved to be the most consistently effective approach, significantly outperforming the entirety of examined methods in terms of EEG recordings epileptic-pattern identification. Indeed, the RF classifier appeared to record an average balanced classification rate (BCR) of 92.38 % in regard to spikes recognition process, and 78.77 % in terms of HFOs detection. Compared to other approaches, our results provide valuable insights into the RF classifier's effectiveness as a powerful ML technique, fit for detecting EEG signals born epileptic bursts. As a potential future work, we envisage to further validate and sustain our major reached findings through incorporating a larger EEG dataset. We also aim to explore the generative adversarial networks (GANs) application so as to generate synthetic EEG signals or combine signal generation techniques with deep learning approaches. Through this new vein of thought, we actually preconize to enhance and boost the automated detection methods' performance even more, thereby, noticeably enhancing the epileptic EEG pattern recognition area.

Added value of advanced workup after the first seizure: A 7-year cohort study.

This study was undertaken to establish whether advanced workup including long-term electroencephalography (LT-EEG) and brain magnetic resonance imaging (MRI) provides an additional yield for the diagnosis of new onset epilepsy (NOE) in patients presenting with a first seizure event (FSE). In this population-based study, all adult (≥16 years) patients presenting with FSE in the emergency department (ED) between March 1, 2010 and March 1, 2017 were assessed. Patients with obvious nonepileptic or acute symptomatic seizures were excluded. Routine EEG, LT-EEG, brain computed tomography (CT), and brain MRI were performed as part of the initial workup. These examinations' sensitivity and specificity were calculated on the basis of the final diagnosis after 2 years, along with the added value of advanced workup (MRI and LT-EEG) over routine workup (routine EEG and CT). Of the 1010 patients presenting with FSE in the ED, a definite diagnosis of NOE was obtained for 501 patients (49.6%). Sensitivity of LT-EEG was higher than that of routine EEG (54.39% vs. 25.5%, p < .001). Similarly, sensitivity of MRI was higher than that of CT (67.98% vs. 54.72%, p = .009). Brain MRI showed epileptogenic lesions in an additional 32% compared to brain CT. If only MRI and LT-EEG were considered, five would have been incorrectly diagnosed as nonepileptic (5/100, 5%) compared to patients with routine EEG and MRI (25/100, 25%, p = .0001). In patients with all four examinations, advanced workup provided an overall additional yield of 50% compared to routine workup. Our results demonstrate the remarkable added value of the advanced workup launched already in the ED for the diagnosis of NOE versus nonepileptic causes of seizure mimickers. Our findings suggest the benefit of first-seizure tracks or even units with overnight EEG, similar to stroke units, activated upon admission in the ED.

Withdrawn: Prevalence and Etiology of Non-Convulsive Status Epilepticus: Significance of Background Activity of the cEEG in Patients Under Hypnosedative Drugs Treatment.

To determine the rate of convulsive/nonconvulsive/ status epilepticus (CSE /NCSE) and predictive value of electroclinical findings of long-term electroencephalography (cEEG) monitoring of these patients and its association with the most commonly used drugs in intensive care units (ICU). We retrospectively collected data of 218 patients whose cEEG was performed in ICU between 2016 and 2017. The cEEG for NCSE diagnosis was evaluated according to Salzburg Consensus Criteria (SCC). EEG background activity, amplitude, minimal-maximal frequencies evaluated and patients' clinical properties, medication use, and glasgow coma scores (GCS) were gathered during EEG recording. The mean age of patients was 57.09 ± 18.9 (16-95) years and 49.1% (107) were female. Of 218 patients, 32 (14.7%) had CSE/NCSE; 38.9% (n = 85) had a seizure/convulsion before EEG recording with 27% (23/85) diagnosed with CSE/NCSE based on cEEG. According to SCC, 20.6% (27/131) were on antiepileptic drugs (AED) and/or hypnosedative drugs (HSD); 5.7% (5/87) who were not taking any drugs had CSE/NCSE findings. For NCSE diagnosis, the cutoff amplitude of the background activity was 37.5 μV with 65.6% sensitivity and 76.2% specificity (AUC = 0.690; p < 0.05) in patients receiving drugs in ICU. Electrophysiological findings frequently appeared in EEGs with a minimum background activity frequency of ≥2.5 Hz (p < 0.001). NCSz/NCSE findings are mostly found in patients who were comatose and had seizure/convulsion history on follow-up. In most cases, NCSz/NCSE diagnosis is missed due to the frequently used drugs in ICU. Electrophysiological findings of NCSE can be determined less frequently in ICU patients under hypnosedative drug treatment with a background activity of cEEG <37 μV.

Gene therapy ameliorates spontaneous seizures associated with cortical neuron loss in a Cln2R207X mouse model.

Although a disease-modifying therapy for classic late infantile neuronal ceroid lipofuscinosis (CLN2 disease) exists, poor understanding of cellular pathophysiology has hampered the development of more effective and persistent therapies. Here, we investigated the nature and progression of neurological and underlying neuropathological changes in Cln2R207X mice, which carry one of the most common pathogenic mutations in human patients but are yet to be fully characterized. Long-term electroencephalography recordings revealed progressive epileptiform abnormalities, including spontaneous seizures, providing a robust, quantifiable, and clinically relevant phenotype. These seizures were accompanied by the loss of multiple cortical neuron populations, including those stained for interneuron markers. Further histological analysis revealed early localized microglial activation months before neuron loss started in the thalamocortical system and spinal cord, which was accompanied by astrogliosis. This pathology was more pronounced and occurred in the cortex before the thalamus or spinal cord and differed markedly from the staging seen in mouse models of other forms of neuronal ceroid lipofuscinosis. Neonatal administration of adeno-associated virus serotype 9-mediated gene therapy ameliorated the seizure and gait phenotypes and prolonged the life span of Cln2R207X mice, attenuating most pathological changes. Our findings highlight the importance of clinically relevant outcome measures for judging preclinical efficacy of therapeutic interventions for CLN2 disease.

Multicenter Study of the Impact of COVID-19 Shelter-In-Place on Tertiary Hospital-based Care for Pediatric Neurologic Disease.

ObjectiveTo describe changes in hospital-based care for children with neurologic diagnoses during the initial 6 weeks following regional Coronavirus 2019 Shelter-in-Place orders.MethodsThis retrospective cross-sectional study of 7 US and Canadian pediatric tertiary care institutions included emergency and inpatient encounters with a neurologic primary discharge diagnosis code in the initial 6 weeks of Shelter-in-Place (COVID-SiP), compared to the same period during the prior 3 years (Pre-COVID). Patient demographics, encounter length, and neuroimaging and electroencephalography use were extracted from the medical record.Results27,900 encounters over 4 years were included. Compared to Pre-COVID, there was a 54% reduction in encounters during Shelter-in-Place. COVID-SiP patients were younger (median 5 years vs 7 years). The incidence of encounters for migraine fell by 72%, and encounters for acute diagnoses of status epilepticus, infantile spasms, and traumatic brain injury dropped by 53%, 55%, and 56%, respectively. There was an increase in hospital length of stay, relative utilization of intensive care, and diagnostic testing (long-term electroencephalography, brain MRI, and head CT (all P<.01)).ConclusionDuring the initial 6 weeks of SiP, there was a significant decrease in neurologic hospital-based encounters. Those admitted required a high level of care. Hospital-based neurologic services are needed to care for acutely ill patients. Precise factors causing these shifts are unknown and raise concern for changes in care seeking of patients with serious neurologic conditions. Impacts of potentially delayed diagnosis or treatment require further investigation.

Epileptic Seizure Detection Based on Bidirectional Gated Recurrent Unit Network

Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate the diagnosis and treatment of epilepsy. Firstly, wavelet transforms are applied to EEG recordings for filtering pre-processing. Then the relative energies of signals in several particular frequency bands are calculated and inputted into Bi-GRU network. Afterwards, the outputs of Bi-GRU network are further processed by moving average filtering, threshold comparison and seizure merging to generate the discriminant results that the tested EEG belong to seizure or not. Evaluated on CHB-MIT scalp EEG database, the proposed seizure detection method obtained an average sensitivity of 93.89% and an average specificity of 98.49%. 124 out of 128 seizures were correctly detected and the achieved average false detection rate was 0.31 per hour on 867.14 h testing data. The results show the superiority of Bi-GRU network in seizure detection and the proposed detection method has a promising potential in the monitoring of long-term EEG.

Interictal autonomic dysfunction in patients with epilepsy

BackgroundAutonomic nervous system (ANS) symptoms are frequently present in people with epilepsy (PwE). They are generally more prominent when they originate from the temporal lobe. We aim to investigate the alterations of autonomic functions during the interictal period in patient with temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) using heart-based tests, blood pressure (BP)-based tests and sympathetic skin response (SSR). Forty-eight PwE with disease duration ranging from 2 to 15 years and 51 healthy individuals were studied. Long-term electroencephalography (EEG) monitoring, the heart rate variability (HRV) during normal breathing, deep breathing, Valsalva maneuver and standing, BP responses during standing, to isometric hand grip and to mental arithmetic, and the SSR was recorded for all participants.Results31 patients with TLE and 17 with IGE showed lower RR-IV values during deep breathing, Valsalva maneuver and standing, but not during rest, impaired BP responses during standing, isometric hand grip, and mental arithmetic. Also, prolonged SSR latencies. Within PwE group, no difference was noticed between males and females, nor between the left and right temporal lobes.ConclusionAbnormal autonomic (sympathetic and parasympathetic) regulatory functions suggest that epilepsy may alter the autonomic function and this is not only in TLE but rather in IGE too. These autonomic changes are irrespective of the localization of epilepsy between the two hemispheres. The ANS changes in epileptic patients, particularly those with autonomic symptoms, confirm that electrophysiologic measures of autonomic function may be of value in preventing sudden unexpected death in epilepsy.

A novel flexible hydrogel electrode with a strong moisturizing ability for long-term EEG recording

Objective. A novel flexible hydrogel electrode with a strong moisturizing ability was prepared for long-term electroencephalography (EEG) monitoring. Approach. The hydrogel was synthesized by polymerizing the N-acryloyl glycinamide monomer. And a proper amount of glycerin was added to the hydrogel to increase the moisture retention ability of the electrodes. The hydrogel shows high mechanical properties, and the liquid in the hydrogel produces a hydrating effect on the skin stratum corneum, which could decrease the contact impedance between skin and electrode. In addition, the installation of hydrogel electrode is very convenient, and the skin of the subject does not need to be abraded. Main results. Scanning electron microscope images show that there are a large number of micropores in the hydrogel, which provide storage space for water molecules. The average potential drift of the hydrogel electrode is relatively low (1.974 ± 0.560 µV min−1). The average contact impedance of hydrogel electrode in forehead region and hair region are 6.43 ± 0.84 kΩ cm2 and 13.15 ± 3.72 kΩ cm2, respectively. The result of open/closed paradigm, steady-state visual evoked potentials, and P300 visual evoked potential show that hydrogel electrode has excellent performance. Compared with the hydrogel without glycerin, the moisture retention ability of hydrogel containing glycerin was greatly improved. Significance. Compared with standard Ag/AgCl wet electrode, hydrogel electrode is more convenient to install and has strong moisture retention ability, which makes it have great potential in daily life for long-term EEG recording.

Phenobarbital versus valproate for generalized convulsive status epilepticus in adults (2): A multicenter prospective randomized controlled trial in China (China 2-P vs. V)

ObjectiveA multicenter study of phenobarbital versus valproate (i.e., the China 2-P vs. V study) was conducted to compare the efficacy and safety of phenobarbital and valproate for generalized convulsive status epilepticus (SE) in a multicenter trial design. MethodsThree improvements (uniform intravenous pumping, pump speed adjustment according to adverse events and blood drug level monitoring) over a previous study were made regarding an intravenous regimen of phenobarbital and valproate in a multicenter, prospective, randomized, controlled study. Long-term electroencephalography (EEG) monitoring was performed after initial drug treatment. Termination, relapse, adverse event and poor prognosis rates in patients with generalized convulsive status epilepticus (GCSE) were compared. ResultsThe rate of GCSE termination within one hour were significantly higher in the phenobarbital group (33 cases) than in the valproate group (36 cases) (84.8 % vs. 63.9 %, P = 0.048), but the rates of nontermination of EEG epileptic discharge within one hour were similar between the two groups (12.1 % vs. 8.3 %, P = 0.702). The relapse and adverse event rates were not significantly different between groups, but 3 hypoventilation events and 1 hypotension event occurred in the phenobarbital group compared to 0 in the valproate group. There were no cases of epileptiform EEG discharge relapse in the phenobarbital group, compared to 1 case in the valproate group. ConclusionsThe phenobarbital regimen evaluated in this study has a higher GCSE termination rate than the valproate regimen, indicating that the former is suitable for countries, regions and individuals with limited access to new antiepileptic drugs or limited economic means.

Long-Term Surgical Outcomes and Predictors of Surgical Treatment in Temporal Lobe Epilepsy

Background: Surgical treatment of epilepsy is a favored modality in the management of refractory temporal lobe epilepsy (TLE) and known to be quite effective. The aim of this study was to evaluate the long-term outcomes in surgical treatment of TLE and to identify prognostic factors influencing postoperative seizure remission. Materials and Methods: The patients who underwent anterior temporal lobectomy (ATL) with the diagnosis of refractory TLE in our center between the years of 2006 and 2020 were included. Preoperative workup process was carried out by a multidisciplinary team and consisted of neuropsychological examination, long-term electroencephalography, brain magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (FDG-PET) for each patient. Postoperative assessment of seizure control was repeated every year following surgery and categorized according to Engel's outcome classification. The predictive value of baseline demographic, clinical, laboratory, imaging, and histopathological parameters on postoperative seizure control was also evaluated. Results: One hundred and forty-three patients who underwent ATL with the diagnosis of TLE were included. According to Engel's classification, the seizure freedom rate was found to be 83.2% at the 2nd year, 81.3% at the 4th year, and 79.2% at the 10th year after surgery. In the univariate analysis, febrile seizure (FS) history, unilateral interictal epileptiform discharges (IEDs), hippocampal sclerosis (HS) on MRI, unilateral temporal hypometabolism (HM) on FDG-PET, and HS in histopathology were predictors of seizure control at the postoperative 2nd and 10th years. FS history, unilateral localization of IEDs, and unilateral FDG-PET findings of temporal HM were found to be the independent predictors of postoperative seizure control on multivariate analysis. Conclusions: In the current study, we revealed that epilepsy surgery provides effective seizure control and represents a beneficial therapeutic option in refractory TLE. Our results also suggested that FS history, unilateral IEDs, and unilateral FDG-PET findings of temporal HM were independent predictors of seizure remission in these patients.

ICU-cEEG Monitoring

ABSTRACT The neurological application of long-term electroencephalography (EEG) monitoring in the intensive care unit (ICU) has been implemented in many healthcare institutions. The use of EEG as a monitoring tool in the ICU affords many potential benefits. Uses include the identification of seizures, vasospasm following subarachnoid hemorrhage (SAH), the assessment of coma and the determination of brain death. Neurologic critical care is focused on recognition and treatment of secondary insults. Often treatment is withheld because these insults are not recognized early enough until an irreversible deficit manifest. Continuous EEG (cEEG) monitoring provides a unique potential to recognize these insults and offers an opportunity for early intervention. Why should we continuously monitor the brain with EEG in the ICU? Nonconvulsive seizures (NCS) are common in comatose patients. Nonconvulsive Status Epilepticus (NCSE) and NCS 1 are damaging to brain tissue; thus, rapid control of seizures is essential to preserving brain function. With the increased use of cEEG in critical care areas, the purpose of this paper is to examine the use and benefits of EEG monitoring of ICU patients, review the indications for the use of cEEG and discuss technical issues and concerns when performing cEEG monitoring. This article has been divided into six distinct sections: (1) Seizures, NCS, and NCSE (2) Periodic Discharges 2 and Patterns on the Ictal-interictal Continuum, (3) Cerebral Ischemia, SAH, and Delayed Cerebral Ischemia (DCI), (4) Encephalopathy and Coma (5) ECI and Brain Death, and (6) ICU-cEEG Monitoring Techniques.

High-frequency electrical stimulation of the anterior thalamic nuclei increases vigilance in epilepsy patients during relaxed and drowsy wakefulness.

High-frequency deep brain stimulation (DBS) of anterior thalamic nuclei (ANT) reduces the frequency and intensity of focal and focal to bilateral tonic-clonic epileptic seizures. We investigated the impact of high-frequency ANT-DBS on vigilance in epilepsy patients during relaxed and drowsy wakefulness, to better understand the effects and the mechanisms of action of this intervention in humans. Four patients with different structural epileptic pathologies were included in this retrospective case-cohort study. Short- and long-term electroencephalography (EEG) was used to determine states of relaxed or drowsy wakefulness and the vigilance changes during stimulation-on and stimulation-off intervals. In relaxed, wakeful patients with eyes closed, the eyelid artifact rate increased acutely and reproducibly during stimulation-on intervals, suggesting an enhanced vigilance. This effect was accompanied by a slight acceleration of the alpha rhythm. In drowsy patients with eyes closed, stimulation generated acutely and reproducibly alpha rhythms, similar to the paradoxical alpha activation during eyes opening. The occurrence of the alpha rhythms reflected an increase in the vigilance of the drowsy subjects during ANT-DBS. This is the first demonstration that ANT-DBS increases the vigilance of wakeful epilepsy patients. Our results deliver circumstantial evidence that high-frequency ANT-DBS activates thalamocortical connections that promote wakefulness.

Forecasting cycles of seizure likelihood.

Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low-risk state, with 14.8% of their time in a high-risk warning state. On average, 69.1% of seizures occurred during high-risk states and 10.5% of seizures occurred in low-risk states. Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.

Large-area MRI-compatible epidermal electronic interfaces for prosthetic control and cognitive monitoring.

Skin-interfaced medical devices are critically important for diagnosing disease, monitoring physiological health and establishing control interfaces with prosthetics, computer systems and wearable robotic devices. Skin-like epidermal electronic technologies can support these use cases in soft and ultrathin materials that conformally interface with the skin in a manner that is mechanically and thermally imperceptible. Nevertheless, schemes so far have limited the overall sizes of these devices to less than a few square centimetres. Here, we present materials, device structures, handling and mounting methods, and manufacturing approaches that enable epidermal electronic interfaces that are orders of magnitude larger than previously realized. As a proof-of-concept, we demonstrate devices for electrophysiological recordings that enable coverage of the full scalp and the full circumference of the forearm. Filamentary conductive architectures in open-network designs minimize radio frequency-induced eddy currents, forming the basis for structural and functional compatibility with magnetic resonance imaging. We demonstrate the use of the large-area interfaces for the multifunctional control of a transhumeral prosthesis by patients who have undergone targeted muscle-reinnervation surgery, in long-term electroencephalography, and in simultaneous electroencephalography and structural and functional magnetic resonance imaging.

Link Prediction Investigation of Dynamic Information Flow in Epilepsy.

As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.