Hippocampal network activity forecasts epileptic seizures
Seizures in people with epilepsy were long thought to occur at random, but recent methods
for seizure forecasting enable estimation of the likelihood of seizure occurrence over short …
for seizure forecasting enable estimation of the likelihood of seizure occurrence over short …
Passive and active markers of cortical excitability in epilepsy
Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for
nearly a century. Its review is performed using qualitative clinical methods that have …
nearly a century. Its review is performed using qualitative clinical methods that have …
A systematic review of cross-patient approaches for EEG epileptic seizure prediction
S Shafiezadeh, GM Duma, M Pozza… - Journal of Neural …, 2024 - iopscience.iop.org
Seizure prediction could greatly improve the quality of life of people suffering from epilepsy.
Modern prediction systems leverage Artificial Intelligence (AI) techniques to automatically …
Modern prediction systems leverage Artificial Intelligence (AI) techniques to automatically …
[HTML][HTML] The spectrum of indications for ultralong-term EEG monitoring
R Rocamora, C Baumgartner, Y Novitskaya… - … : European Journal of …, 2024 - Elsevier
Purpose We assessed clinical cases to investigate the spectrum of indications for ultra-
longterm EEG monitoring using a subcutaneous implantable device in adult patients with …
longterm EEG monitoring using a subcutaneous implantable device in adult patients with …
SzCORE: Seizure Community Open‐Source Research Evaluation framework for the validation of electroencephalography‐based automated seizure detection …
The need for high‐quality automated seizure detection algorithms based on
electroencephalography (EEG) becomes ever more pressing with the increasing use of …
electroencephalography (EEG) becomes ever more pressing with the increasing use of …
SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms
The need for high-quality automated seizure detection algorithms based on
electroencephalography (EEG) becomes ever more pressing with the increasing use of …
electroencephalography (EEG) becomes ever more pressing with the increasing use of …
Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction
JD Chambers, MJ Cook, AN Burkitt… - Frontiers in …, 2024 - frontiersin.org
Objective Seizure prediction could improve quality of life for patients through removing
uncertainty and providing an opportunity for acute treatments. Most seizure prediction …
uncertainty and providing an opportunity for acute treatments. Most seizure prediction …
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: a systematic review
Machine learning algorithms for seizure detection have shown great diagnostic potential,
with recent reported accuracies reaching 100%. However, few published algorithms have …
with recent reported accuracies reaching 100%. However, few published algorithms have …
Epileptic seizure forecasting with wearable‐based nocturnal sleep features
TY Ding, L Gagliano, A Jahani, DH Toffa… - Epilepsia …, 2024 - Wiley Online Library
Objective Non‐invasive biomarkers have recently shown promise for seizure forecasting in
people with epilepsy. In this work, we developed a seizure‐day forecasting algorithm based …
people with epilepsy. In this work, we developed a seizure‐day forecasting algorithm based …
Topological analysis of brain dynamical signals reveals signatures of seizure susceptibility
Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods).
However, whether epilepsy may alter brain dynamics during background (non-ictal) periods …
However, whether epilepsy may alter brain dynamics during background (non-ictal) periods …