Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach.
Georgios NtolkerasNavaneethakrishna MakaramMatteo BernabeiAime Cristina De La VegaJeffrey BoltonJoseph R MadsenScellig S D StonePhillip L PearlChristos PapadelisEllen P GrantEleonora TamiliaPublished in: Epilepsia (2024)
We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.
Keyphrases
- drug resistant
- resting state
- machine learning
- functional connectivity
- multidrug resistant
- acinetobacter baumannii
- end stage renal disease
- ejection fraction
- working memory
- big data
- oxidative stress
- artificial intelligence
- electronic health record
- white matter
- multiple sclerosis
- pseudomonas aeruginosa
- patient reported
- human health