Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy

SEEG in 2025: progress and pending challenges in stereotaxy methods, biomarkers and radiofrequency thermocoagulation

PURPOSE OF REVIEW

Technological innovations and clinical research in SEEG have dramatically increased with its worldwide dissemination. In this review, we summarize the main advances in the field from the last 5 years.

RECENT FINDINGS

Several large series and meta-analyses have provided consistent data regarding a lower risk of serious complications with SEEG as compared to sub-dural grids, while some studies also suggest a greater diagnostic value. The safety and precision of SEEG partly depends on the type of vascular imaging and method of implantation, with some series suggesting that MR angiography might not provide an optimal delineation of electrode-vessel conflicts and that frameless stereotaxy lacks precision. Noninvasive frame coupled with robot-guided implantation might offer the best precision/invasiveness tradeoff. Small series suggest that SEEG can be safely performed from the age of 16 months, and that adding electrodes during SEEG often prove beneficial. Transhemispheric electrodes targeting the mesial frontal structures, bilaterally, proved safe and informative. Several interictal and ictal biomarkers of the epileptogenic zone have been investigated. Although high-frequency oscillations (HFOs) remain a biomarker of interest, a randomized controlled trial failed to demonstrate its diagnostic value against spikes. Furthermore, other interictal biomarkers proved to better correlate with the epileptogenic zone than HFOs rate, including spike-gamma and spike-ripples. Ictal biomarkers of interest include the so-called chirp and epileptogenic zone fingerprint. Overall, recent data suggest that high-frequency activities are not a mandatory feature of interictal and ictal biomarkers of the epileptogenic zone. Radiofrequency thermocoagulation (RFTC) performed during SEEG investigation have also progressed, with some authors reporting spectacular rates of seizure freedom in patients with localized epileptogenic lesion but also mesial temporal sclerosis. However, a systematic assessment of memory and mental health demonstrated the presence of altered memory and psychiatric complications in a significant proportion of mesial temporal lobe RFTC.

SUMMARY

Progress has been made in the technology and methods used to perform SEEG and RFTC, with the view to increase safety and effectiveness. Several interictal and ictal biomarkers appear promising but still face challenges in their validation and implementation in clinical practice. Future research requires harmonization in the concepts of the seizure onset and epileptogenic zones, and prospective pathology-specific studies.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Recent advances in clinical electroencephalography

PURPOSE OF REVIEW

Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients.

RECENT FINDINGS

Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field.

SUMMARY

This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.