Machine learning methods applied to Cortico-cortical evoked potentials aid in localizing seizure onset zones

IG Malone, KE Smith, ME Urdaneta, TS Davis… - arXiv preprint arXiv …, 2022 - arxiv.org
Epilepsy affects millions of people, reducing quality of life and increasing risk of premature
death. One-third of epilepsy cases are drug-resistant and require surgery for treatment …

Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in focal epilepsy

B Yang, B Zhao, C Li, J Mo, Z Guo, Z Li, Y Yao… - Clinical …, 2024 - Elsevier
Objective We aimed to develop a new approach for identifying the localization of the seizure
onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the …

Localizing temporal lobe seizure onset zones using deep learning on SEEG cortico-cortical evoked potentials

GW Johnson, LY Cai, DJ Doss, JW Jiang, AS Negi… - bioRxiv, 2022 - biorxiv.org
In drug resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ)
localization using brief interictal recordings would supplement presurgical evaluations and …

Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a Transformer

J Norris, A Chari, G Cooray, M Tisdall, K Friston… - arXiv preprint arXiv …, 2024 - arxiv.org
Epilepsy is one of the most common neurological disorders, and many patients require
surgical intervention when medication fails to control seizures. For effective surgical …

Integrating Data Across Oscillatory Power Bands Predicts the Epileptogenic Zone: the Frequency Range Explorer Epileptogenic Zone (FREEZ) Identification Algorithm

S O'Leary, AC Lesage, L Camarillo-Rodriguez, O Zhou… - bioRxiv, 2024 - biorxiv.org
Epilepsy affects over 70 million people globally. One-third of people with focal epilepsy have
drug-resistant epilepsy. Identification and removal of the site of onset of seizures, termed the …

A one-dimensional convolutional neural network model for automated localization of epileptic foci

B Li, X Zhao, Q Zhao, T Tanaka… - 2019 Asia-Pacific Signal …, 2019 - ieeexplore.ieee.org
Intracranial electrocorticogram (iEEG) is often used by clinical experts to determine the
location of the epileptic focal in the treatment of epilepsy. However, assess the location of …

[PDF][PDF] SZLoc: a multi-resolution architecture for automated epileptic seizure localization from scalp EEG

J Craley, E Johnson, CC Jouny, D Hsu… - Medical Imaging with …, 2022 - par.nsf.gov
We propose an end-to-end deep learning framework for epileptic seizure localization from
scalp electroencephalography (EEG). Our architecture, SZLoc, extracts multi-resolution …

Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy

Y Varatharajah, B Berry, J Cimbalnik… - Journal of neural …, 2018 - iopscience.iop.org
Objective. An ability to map seizure-generating brain tissue, ie the seizure onset zone (SOZ),
without recording actual seizures could reduce the duration of invasive EEG monitoring for …

SozRank: A new approach for localizing the epileptic seizure onset zone

Y Murin, J Kim, J Parvizi… - PLoS computational …, 2018 - journals.plos.org
Epilepsy is one of the most common neurological disorders affecting about 1% of the world
population. For patients with focal seizures that cannot be treated with antiepileptic drugs …

[HTML][HTML] Identifying seizure onset zone from electrocorticographic recordings: a machine learning approach based on phase locking value

B Elahian, M Yeasin, B Mudigoudar, JW Wheless… - Seizure, 2017 - Elsevier
Purpose Using a novel technique based on phase locking value (PLV), we investigated the
potential for features extracted from electrocorticographic (ECoG) recordings to serve as …