Epilepsia Open最新文献

Objective: To assess the feasibility, technical performance, and safety of a novel endovascular electroencephalogram (eEEG) electrode, EP-01, designed for minimally invasive seizure localization in patients with drug-resistant epilepsy.

Methods: This single-center, prospective, exploratory trial enrolled five patients with drug-resistant epilepsy undergoing the Wada test. The EP-01 electrode, featuring a platinum monopolar tip, was inserted into the venous sinuses using a microcatheter, and simultaneous recording of eEEG and scalp electroencephalography (EEG) signals was conducted. The primary outcome was the feasibility of eEEG signal acquisition, whereas the secondary outcomes included a signal sensitivity comparison between scalp and eEEG recordings, technical success assessment, and safety evaluation.

Results: Successful eEEG recording was achieved in all five patients (mean age 34.2 years, 2 women) experiencing focal impaired awareness seizures. The EP-01 electrode successfully captured EEG signals, detecting 158 interictal epileptiform discharges (IEDs). eEEG demonstrated significantly higher amplitudes (median: 130.4 μV) compared with scalp EEG (median: 0 μV), with 57.0% of IEDs undetectable by scalp EEG (p < 0.001). EEG changes during eye opening/closing were consistently recorded by eEEG. The technical success rate was 100%, with an average of 4.5 eEEG electrodes placed per patient. The median displacement of electrode tips during neck rotation was 3.45 mm; hemorrhagic or thrombotic complications were absent; and all devices were safely removed.

Significance: In this short-term study, the EP-01 eEEG device demonstrated high feasibility and safety for minimally invasive EEG acquisition in patients with epilepsy. To the best of our knowledge, this study provides the first evidence of IED detection in multiple patients using an eEEG system tailored for epilepsy monitoring, underscoring EP-01's potential for localizing seizure foci. Moreover, the capability to place multiple electrodes improves its utility in comprehensive epileptogenic mapping. Given the limited recording duration, long-term studies are necessary to validate EP-01's clinical efficacy and safety.

Plain language summary: This study tested EP-01, a new device that records brain activity from inside the blood vessels, unlike traditional scalp EEG. In five people with epilepsy, the device safely captured stronger seizure-related signals than the traditional EEG. Moreover, the device could be placed and removed without complications. This less invasive method may help doctors more accurately find where seizures begin in the brain.

Objective: The integration of neurotechnology and artificial intelligence (AI) in epilepsy research has led to significant advancements in diagnosis, monitoring, and treatment. However, the impact of these innovations is often diminished by inadequate and inaccurate reporting, limiting their reproducibility and implementation. This study aimed to identify common peer review concerns and develop reporting recommendations specific to neurotechnology and AI studies in epilepsy.

Methods: We conducted a qualitative analysis of peer review comments from original research article submissions to Epilepsia Open over a 2-year period (September 2021-August 2023). We selected manuscripts that focused on neurotechnology or AI applications in epilepsy, excluding those using standard clinical technologies or conventional statistical analyses. Reviewer comments were classified using a validated checklist, categorizing issues into themes and subthemes. Based on recurrent peer review concerns, we developed a set of reporting recommendations for neurotechnology and AI studies.

Results: Among 329 manuscripts sent for peer review, 67 were classified as neurotechnology or AI studies and included in the analysis. These studies predominantly involved advanced neuroimaging analysis, advanced electroencephalography (EEG) analysis, and neuromodulation systems. Reviewer comments were primarily focused on study methodology (37%), manuscript presentation (19%), discussion (17%), and results (12%). Based on peer review comments, we formulated reporting recommendations, hoping to enhance study transparency, methodological rigor, and reproducibility.

Significance: Our reporting recommendations address key concerns raised during peer review, providing guidance to authors and reviewers to improve the quality and clarity of neurotechnology and AI research in epilepsy. These recommendations complement existing reporting standards and contribute to the advancement of robust and impactful research in the field.

Plain language summary: We studied how researchers report studies on neurotechnology and AI in epilepsy. Many studies face problems during peer review, such as unclear methods, weak study rationale, and errors in statistics or citations. We analyzed reviewer feedback and created recommendations to improve how these studies are reported. Our goal is to help researchers develop and present their work more clearly and accurately, making it easier for others to understand and build upon their findings. This can lead to better use of AI and neurotechnology in epilepsy research and care.

Objective: This study evaluated the performance of the ENCEVIS artificial intelligence (AI)-based algorithm as a screening tool to predict the presence of ictal and/or interictal epileptiform discharges (IEDs) in electroencephalography (EEG) recordings.

Methods: This prospective study was conducted from 2019 to 2023 at Khechinashvili University Hospital, Tbilisi. EEG recordings over 3 h were included; standard EEGs and recordings with EEG-negative seizures were excluded. Two independent EEG experts performed blinded visual analyses. In case of disagreement, a third neurophysiologist was consulted, and the final consensus served as the reference standard. ENCEVIS annotations were compared to this reference.

Results: A total of 267 EEG recordings were analyzed. Clinical events occurred in 54 patients (20.2%): 43 had epileptic seizures, 11 had nonepileptic events, and 2 had both. A total of 114 seizures were captured, of which ENCEVIS correctly detected 65 (sensitivity 57.0%, p > 0.05). Detection sensitivity varied by seizure type, FB-TCS bilateral and GTCS 100%, focal seizures with impaired consciousness 66.7% (median 50 s), for focal seizures with preserved consciousness 36.4% (median 28 s), and for tonic seizures 23.1% (median 11 s). Longer seizure duration was associated with higher detection rates. False positive seizure detection rate was 0.27/h. ENCEVIS detected at least one seizure in 42 of 43 seizure-positive recordings (97.7%). Specificity was 48.2%, a positive predictive value (PPV) of 26.6%, and a negative predictive value (NPV) of 99.1%. Performance for interictal detection demonstrated a sensitivity of 97.4%, a specificity of 40.2%, a PPV of 69.3%, and an NPV of 91.8%.

Significance: The ENCEVIS algorithm demonstrates high sensitivity in detecting EEG recordings with ictal and interictal epileptiform activity. However, its limited specificity necessitates neurophysiological review to validate positive findings. Its high NPV highlights ENCEVIS's potential as a prescreening tool for identifying EEG recordings without ictal or interictal abnormalities, thereby reducing the workload on neurophysiologists.

Plain language summary: This study evaluated the ENCEVIS artificial intelligence (AI) algorithm as a tool to support electroencephalography (EEG) analysis in epilepsy care. Researchers analyzed 267 long-term EEG recordings and compared ENCEVIS results to expert neurophysiologists' evaluations. The algorithm showed high accuracy in detecting normal EEGs and most seizures, especially longer ones. It also performed well in identifying interictal epileptiform activity. However, ENCEVIS sometimes incorrectly annotated normal recordings as abnormal. While it cannot replace expert review, ENCEVIS may serve as a helpful screening tool to reduce the time experts spend reviewing EEGs without epileptic activity.