Routine and sleep EEG: Minimum recording standards of the International Federation of Clinical Neurophysiology and the International League Against Epilepsy

Improving epilepsy diagnosis across the lifespan: approaches and innovations

Epilepsy diagnosis is often delayed or inaccurate, exposing people to ongoing seizures and their substantial consequences until effective treatment is initiated. Important factors contributing to this problem include delayed recognition of seizure symptoms by patients and eyewitnesses; cultural, geographical, and financial barriers to seeking health care; and missed or delayed diagnosis by health-care providers. Epilepsy diagnosis involves several steps. The first step is recognition of epileptic seizures; next is classification of epilepsy type and whether an epilepsy syndrome is present; finally, the underlying epilepsy-associated comorbidities and potential causes must be identified, which differ across the lifespan. Clinical history, elicited from patients and eyewitnesses, is a fundamental component of the diagnostic pathway. Recent technological advances, including smartphone videography and genetic testing, are increasingly used in routine practice. Innovations in technology, such as artificial intelligence, could provide new possibilities for directly and indirectly detecting epilepsy and might make valuable contributions to diagnostic algorithms in the future.

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People

OBJECTIVE

Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG.

METHODS AND PROCEDURES

The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches.

RESULTS

Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process.

CONCLUSION

Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques.

CLINICAL IMPACT

An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

Sleep spindles as neurophysiological biomarkers of schizophrenia

Schizophrenia (SCZ) is a complex psychiatric disorder characterized by a wide range of clinical symptoms, including disrupted sleep. In recent years, there has been growing interest in assessing alterations in sleep parameters in patients with SCZ. Sleep spindles are brief (0.5-2 s) bursts of 12- to 16-Hz rhythmic electroencephalogram (EEG) oscillatory activity occurring during non-rapid eye movement (NREM) sleep. Spindles have been implicated in several critical brain functions, including learning, memory and plasticity, and are thought to reflect the integrity of underlying thalamocortical circuits. This review aims to provide an overview of the current research investigating sleep spindles in SCZ. After briefly describing the neurophysiological features of sleep spindles, I will discuss alterations in spindle characteristics observed in SCZ, their associations with the clinical symptomatology of these patients and their putative underlying neuronal and molecular mechanisms. I will then discuss the utility of sleep spindle measures as predictors of treatment response and disease progression. Finally, I will highlight future directions for research in this emerging field, including the prospect of utilizing sleep spindles as neurophysiological biomarkers of SCZ.

Seizure provocation in EEG recordings: A data-driven approach

OBJECTIVE

Recording seizures on video-EEG has a high diagnostic value. However, bilateral convulsive seizures constitute a risk for the patients. Our aim was to investigate the diagnostic yield and associated risks of provocation methods in short-term video-EEGs.

METHODS

We extracted data on seizures and provocation methods from a large database of short-term video-EEGs with standardized annotations using SCORE (Standardized Computer-based Organized reporting of EEG).

RESULTS

2742 paroxysmal clinical episodes were recorded in 11 919 consecutive EEGs. Most epileptic seizures (54%) were provoked. Hyperventilation provoked most of typical absence seizures (55%), intermittent photic stimulation (IPS) provoked myoclonic seizures (25%) and most of bilateral convulsive seizures (55%), while 43% of focal seizures were precipitated by sleep. All but one of the 16 bilateral convulsive seizures were provoked by IPS or sleep. Latency between start of generalized photoparoxysmal EEG response and bilateral convulsive seizures were ≤3 s in all but one patient.

SIGNIFICANCE

The large, structured database provides evidence for the diagnostic utility of various provocation methods in short-term video-EEGs. The risk of bilateral convulsive seizures is relatively small, but it cannot be prevented by stopping IPS after 3 s. A priori knowledge about seizure semiology helps planning patient-tailored provocation strategy in short-term video-EEGs.

Single-trial EEG analysis reveals burst structure during photic driving

OBJECTIVE

Photic driving in the human visual cortex evoked by intermittent photic stimulation is usually characterized in averaged data by an ongoing oscillation showing frequency entrainment and resonance phenomena during the course of stimulation. We challenge this view of an ongoing oscillation by analyzing unaveraged data.

METHODS

64-channel EEGs were recorded during visual stimulation with light flashes at eight stimulation frequencies between 7.8 and 23 Hz for fourteen healthy volunteers. Time-frequency analyses were performed in averaged and unaveraged data.

RESULTS

While we find ongoing oscillations in the averaged data during intermittent photic stimulation, we find transient events (bursts) of activity in the unaveraged data. Both resonance and entrainment occur for the ongoing oscillations in the averaged data and the bursts in the unaveraged data.

CONCLUSIONS

We argue that the continuous oscillations in the averaged signal may be composed of brief, transient bursts in single trials. Our results can also explain previously observed amplitude fluctuations in averaged photic driving data.

SIGNIFICANCE

Single-trial analyses might consequently improve our understanding of resonance and entrainment phenomena in the brain.

Initiation of Antiseizure Medications by US Board-Certified Neurologists After a First Unprovoked Seizure Based on EEG Findings

Background and Objectives

To investigate neurologists' practice variability in antiseizure medication (ASM) initiation after a first unprovoked seizure based on reported EEG interpretations.

Methods

We developed a 15-question multiple-choice survey incorporating a standardized clinical case scenario of a patient with a first unprovoked seizure for whom different EEG reports were provided. The survey was distributed among board-certified neurologists practicing in the United States. Associations between categorical variables were evaluated using the Fisher Exact test. Multivariate analysis was performed using logistic regression.

Results

A total of 106 neurologists responded to the survey. Most responders (75%-95%) would start ASM for definite epileptiform features on EEG, with similar rates between subgroups differing in years of practice, presence of subspecialty EEG training, and self-reported confidence in EEG interpretation. There was greater variability in practice for nonspecific EEG abnormalities, with sharply contoured activity, sharp transients, and focal delta slowing associated with the highest variability and uncertainty. Neurologists with >5 years of practice experience (21% vs 44%, OR 0.35 [95% CI 0.13-0.89], p = 0.021), subspecialty EEG training (15% vs 50%, OR = 0.17 [95% CI 0.06-0.48], p < 0.001), and greater confidence in EEG interpretation (21% vs 52%, OR 0.24 [95% CI 0.09-0.62], p = 0.001) were less likely to start ASM for ≥2 nonspecific EEG abnormalities and reported greater uncertainty. In multivariate analysis, seniority (p = 0.039) and subspecialty EEG training (p = 0.032) were associated with decreased ASM initiation for nonspecific EEG features.

Discussion

There was substantial variability in ASM initiation practices between board-certified neurologists after a first unprovoked seizure with nonspecific EEG abnormalities. These findings clarify specific areas where EEG reporting may be optimized and reinforces the importance of implementing evidence-based practice guidelines.

Post-hypoxic status epilepticus - A distinct subtype of status epilepticus with poor prognosis

OBJECTIVE

To evaluate the clinical outcome of patients with possible and definitive post-hypoxic status epilepticus (SE) and to describe the SE types in patients with definitive post-hypoxic SE.

METHODS

Patients with definitive or possible SE resulting from hypoxic brain injury after cardiac arrest (CA) were prospectively recruited. Intermittent EEG was used for the diagnosis of SE according to clinical practice. Two raters blinded to outcome analyzed EEGs retrospectively for possible and definitive SE patterns and background features (frequency, continuity, reactivity, and voltage). Definitive SE was classified according to semiology (ILAE). Mortality and Cerebral Performance Categories (CPC) score were evaluated 1 month after CA.

RESULTS

We included 64 patients of whom 92% died. Among the survivors, only one patient had a good neurological outcome (CPC 1). No patient survived with a burst suppression pattern, low voltage, or electro-cerebral silence in any EEG. Possible or definitive SE was diagnosed in a median of 47 h (IQR 39-72 h) after CA. EEG criteria for definitive electrographic SE were fulfilled in 39% of patients; in 38% - for electroclinical SE and in 23% - for ictal-interictal continuum (IIC). The outcome did not differ significantly between the three groups. The only patient with good functional outcome belonged to the IIC group. Comatose non-convulsive SE (NCSE) without subtle motor phenomenon occurred in 20% of patients with definitive electrographic SE and outcome was similar to other types of SE.

SIGNIFICANCE

Possible or definitive SE due to hypoxic brain injury is associated with poor prognosis. The outcome of patients with electrographic SE, electroclinical SE, and IIC did not differ significantly. Outcome was similar in patients with definitive electrographic SE with and without prominent motor features.

DICOM® integrated EEG data: A first clinical implementation of the new DICOM standard for neurophysiology data

OBJECTIVE

Demonstrating a pilot implementation of the Digital Imaging and Communication in Medicine (DICOM) neurophysiology standard published in 2020.

METHODS

An automated workflow for converting EEG data from a proprietary vendor EEG format to standardized and interoperable DICOM format was developed and tested.

RESULTS

Retrieval of proprietary EEG data, associated videos, annotations and metadata from the vendor EEG archive and their subsequent conversion to DICOM EEG was possible without changes to the departmental workflow. To transfer DICOM EEG data to the central radiology DICOM archive, only minor extensions in the parameterization of the archive's DICOM interfaces were necessary. Linkage with the electronic health record (EHR) and display in a DICOM EEG viewer could be demonstrated. A random sample of 88 DICOM EEG studies was compared to the original vendor files and EEG and video file sizes were comparable.

CONCLUSIONS

Storing and reviewing EEG data in standardized DICOM format is feasible, facilitated by existing DICOM infrastructure, and therefore allows for vendor independent access to EEG data.

SIGNIFICANCE

We report the first implementation of the DICOM neurophysiology standard, thus promoting standardization in the field of neurophysiology as well as data exchange and access to legacy recordings in an interoperable vendor independent format.

Integrating Neuromonitoring in Pediatric Emergency Medicine: Exploring Two Options for Point-of-Care Electroencephalogram (pocEEG) via Patient Monitors-A Technical Note

Central nervous system (CNS) disorders are among the most frequent presentations in critically ill children. Status epilepticus (SE) is a frequent scenario in the resuscitation bay. In patients with altered mental status, non-convulsive SE (NCSE) is often underrecognized and critically impacts the neurological outcome and duration of hospitalization. An electroencephalogram (EEG) is required to diagnose NCSE. However, standard EEG recordings are time- and staff-intensive, and their availability is limited, especially outside regular working hours. We aimed to improve patient care by developing a simplified EEG recording method, using a reduced lead montage (point-of-care EEG-pocEEG), that is suitable for use in pediatric emergency departments. The objective was to devise a cost-effective unit with low space requirements that fitted the existing technical infrastructure. We present two technical options for clinical pocEEG acquisition using patient monitors (GE Carescape, Philips IntelliVue) that enable data collection for educational and research purposes. A simplified, rapid response EEG like the pocEEG enables neuromonitoring of patients with CNS disorders in pediatric emergency settings, facilitating timely diagnosis and treatment initiation when standard EEG is not readily available.

Melatonin versus Sleep Deprivation for Sleep Induction in Nap Electroencephalography: Protocol for a Prospective Randomized Crossover Trial in Children and Young Adults with Epilepsy

Electroencephalography (EEG) continues to be a pivotal investigation in children with epilepsy, providing diagnostic evidence and supporting syndromic classification. In the pediatric population, electroencephalographic recordings are frequently performed during sleep, since this procedure reduces the number of artifacts and activates epileptiform abnormalities. To date, no shared guidelines are available for sleep induction in EEG. Among the interventions used in the clinical setting, melatonin and sleep deprivation represent the most used methods. The main purpose of this study is to test the non-inferiority of 3–5 mg melatonin versus sleep deprivation in achieving sleep in nap electroencephalography in children and young adult patients with epilepsy. To test non-inferiority, a randomized crossover trial is proposed where 30 patients will be randomized to receive 3–5 mg melatonin or sleep deprivation. Each enrolled subject will perform EEG recordings during sleep in the early afternoon for a total of 60 EEGs. In the melatonin group, the study drug will be administered a single oral dose 30 min prior to the EEG recording. In the sleep deprivation group, parents will be required to subject the child to sleep deprivation the night before registration. Urinary and salivary concentrations of melatonin and of its main metabolite 6-hydroxymelatonin will be determined by using a validated LC-MS method. The present protocol aims to offer a standardized protocol for sleep induction to be applied to EEG recordings in those of pediatric age. In addition, melatonin metabolism and elimination will be characterized and its potential interference in interictal abnormalities will be assessed.