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| Content Provider | frontiers |
|---|---|
| Author | Quitadamo, Lucia R. Foley, Elaine Mai, Roberto de Palma, Luca Specchio, Nicola Seri, Stefano |
| Abstract | The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias and computational time. In this manuscript we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space MEG data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment. |
| ISSN | 16625196 |
| DOI | 10.3389/fninf.2018.00045 |
| Volume Number | 12 |
| Journal | Frontiers in Neuroinformatics |
| Language | English |
| Publisher Date | 2018-07-11 |
| Access Restriction | Open |
| Subject Keyword | Epilepsy EEGLAB Seizure-onset zone Stereo-EEG. High-frequency oscillations IEEG |
| Content Type | Text |
| Resource Type | Article |
| Subject | Neuroscience Biomedical Engineering Computer Science Applications |
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