Title :
Automated localization of the seizure focus using interictal intracranial EEG
Author :
Jing Jin ; Dauwels, Justin ; Cash, Sydney
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
Abstract :
Up to 30% of epileptic patients have seizures poorly controlled with anti-epileptic drugs alone. Surgical therapy might be beneficial to patients who respond poorly to drug treatments. It is therefore crucial to accurately localize the seizure focus. Neurologists rely heavily on seizures to determine the focus. The invasive recordings usually continue for days or weeks, which is costly and entails significant risk for the patients. In this paper, techniques are developed to localize the seizure focus using brief interictal intracranial EEG (iEEG). A supervised learning paradigm is utilized making use of features extracted from interictal iEEG on multiple referential montages. Analysis of 14 epileptic patients (implanted with depth electrodes) shows that iEEG features such as slowing, ripples, spikes, and local synchrony measures are strongly correlated to the seizure focus. These procedures may allow reliable localization of the seizure focus from brief interictal iEEG, which in turn may lead to shorter hospitalizations.
Keywords :
biomedical electrodes; electroencephalography; feature extraction; learning (artificial intelligence); medical disorders; medical signal processing; automated localization; depth electrodes; epileptic patient; feature extraction; iEEG; interictal intracranial EEG; seizure focus; supervised learning paradigm; Educational institutions; Electrodes; Electroencephalography; Epilepsy; Hafnium compounds; Oscillators; Sensitivity;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
DOI :
10.1109/EMBC.2014.6944609