EEG biomarker candidates for the identification of epilepsy

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review

Background

Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

Methods

We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

Results

We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

Conclusion

The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

Significance

We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Epilepsy as a Novel Phenotype of BPTF-Related Disorders

BACKGROUND

Neurodevelopmental disorder with dysmorphic facies and distal limb anomalies (NEDDFL) is associated to BPTF gene haploinsufficiency. Epilepsy was not included in the initial descriptions of NEDDFL, but emerging evidence indicates that epileptic seizures occur in some affected individuals. This study aims to investigate the electroclinical epilepsy features in individuals with NEDDFL.

METHODS

We enrolled individuals with BPTF-related seizures or interictal epileptiform discharges (IEDs) on electroencephalography (EEG). Demographic, clinical, genetic, raw EEG, and neuroimaging data as well as response to antiseizure medication were assessed.

RESULTS

We studied 11 individuals with a null variant in BPTF, including five previously unpublished ones. Median age at last observation was 9 years (range: 4 to 43 years). Eight individuals had epilepsy, one had a single unprovoked seizure, and two showed IEDs only. Key features included (1) early childhood epilepsy onset (median 4 years, range: 10 months to 7 years), (2) well-organized EEG background (all cases) and brief bursts of spikes and slow waves (50% of individuals), and (3) developmental delay preceding seizure onset. Spectrum of epilepsy severity varied from drug-resistant epilepsy (27%) to isolated IEDs without seizures (18%). Levetiracetam was widely used and reduced seizure frequency in 67% of the cases.

CONCLUSIONS

Our study provides the first characterization of BPTF-related epilepsy. Early-childhood-onset epilepsy occurs in 19% of subjects, all presenting with a well-organized EEG background associated with generalized interictal epileptiform abnormalities in half of these cases. Drug resistance is rare.

Current challenges in focal epilepsy treatment: An Italian Delphi consensus

BACKGROUND

Epilepsy, a globally prevalent neurological condition, presents distinct challenges in management, particularly for focal-onset types. This study aimed at addressing the current challenges and perspectives in focal epilepsy management, with focus on the Italian reality.

METHODS

Using the Delphi methodology, this research collected and analyzed the level of consensus of a panel of Italian epilepsy experts on key aspects of focal epilepsy care. Areas of focus included patient flow, treatment pathways, controlled versus uncontrolled epilepsy, follow-up protocols, and the relevance of patient-reported outcomes (PROs). This method allowed for a comprehensive assessment of consensus and divergences in clinical opinions and practices.

RESULTS

The study achieved consensus on 23 out of 26 statements, with three items failing to reach a consensus. There was strong agreement on the importance of timely intervention, individualized treatment plans, regular follow-ups at Epilepsy Centers, and the role of PROs in clinical practice. In cases of uncontrolled focal epilepsy, there was a clear inclination to pursue alternative treatment options following the failure of two previous therapies. Divergent views were evident on the inclusion of epilepsy surgery in treatment for uncontrolled epilepsy and the routine necessity of EEG evaluations in follow-ups. Other key findings included concerns about the lack of pediatric-specific research limiting current therapeutic options in this patient population, insufficient attention to the transition from pediatric to adult care, and need for improved communication. The results highlighted the complexities in managing epilepsy, with broad consensus on patient care aspects, yet notable divergences in specific treatment and management approaches.

CONCLUSION

The study offered valuable insights into the current state and complexities of managing focal-onset epilepsy. It highlighted many deficiencies in the therapeutic pathway of focal-onset epilepsy in the Italian reality, while it also underscored the importance of patient-centric care, the necessity of early and appropriate intervention, and individualized treatment approaches. The findings also called for continued research, policy development, and healthcare system improvements to enhance epilepsy management, highlighting the ongoing need for tailored healthcare solutions in this evolving field.

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Causal link between prefrontal cortex and EEG microstates: evidence from patients with prefrontal lesion

Introduction

At present, elucidating the cortical origin of EEG microstates is a research hotspot in the field of EEG. Previous studies have suggested that the prefrontal cortex is closely related to EEG microstate C and D, but whether there is a causal link between the prefrontal cortex and microstate C or D remains unclear.

Methods

In this study, pretrial EEG data were collected from ten patients with prefrontal lesions (mainly located in inferior and middle frontal gyrus) and fourteen matched healthy controls, and EEG microstate analysis was applied.

Results

Our results showed that four classical EEG microstate topographies were obtained in both groups, but microstate C topography in patient group was obviously abnormal. Compared to healthy controls, the average coverage and occurrence of microstate C significantly reduced. In addition, the transition probability from microstate A to C and from microstate B to C in patient group was significantly lower than those of healthy controls.

Discussion

The above results demonstrated that the damage of prefrontal cortex especially inferior and middle frontal gyrus could lead to abnormalities in the spatial distribution and temporal dynamics of microstate C not D, showing that there is a causal link between the inferior and middle frontal gyrus and the microstate C. The significance of our findings lies in providing new evidence for elucidating the cortical origin of microstate C.