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
- end stage renal disease
- multidrug resistant
- acinetobacter baumannii
- ejection fraction
- chronic kidney disease
- working memory
- newly diagnosed
- big data
- oxidative stress
- prognostic factors
- peritoneal dialysis
- artificial intelligence
- electronic health record
- risk assessment
- patient reported outcomes
- multiple sclerosis
- young adults
- deep learning
- human health
- data analysis