NDLI: Automated localization of the seizure focus using interictal intracranial EEG

Content Provider	IEEE Xplore Digital Library
Author	Jing Jin Dauwels, J. Cash, S.
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Jing Jin; Dauwels, J.) \|\| Neurology Dept., Massachusetts Gen. Hosp., Boston, MA, USA (Cash, S.)
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.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	4439
Ending Page	4442
File Size	624424
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944609
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Hafnium compounds Electrodes Epilepsy Educational institutions Sensitivity Oscillators
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Computing network-based features from intracranial EEG time series data: Application to seizure focus localization

Probing cortical excitability using cross-frequency coupling in intracranial EEG recordings: A new method for seizure prediction

User-guided interictal spike detection

Automatic ictal HFO detection for determination of initial seizure spread

Seizure prediction in intracranial EEG: A patient-specific rule-based approach

Brain dynamics based automated epileptic seizure detection

Localization of seizure onset area from intracranial non-seizure EEG by exploiting locally enhanced synchrony

Spatial correlation based artifact detection for automatic seizure detection in EEG

Automated epilepsy diagnosis using interictal scalp EEG

Automated localization of the seizure focus using interictal intracranial EEG

Similar Documents

Computing network-based features from intracranial EEG time series data: Application to seizure focus localization

Probing cortical excitability using cross-frequency coupling in intracranial EEG recordings: A new method for seizure prediction

User-guided interictal spike detection

Automatic ictal HFO detection for determination of initial seizure spread

Seizure prediction in intracranial EEG: A patient-specific rule-based approach

Brain dynamics based automated epileptic seizure detection

Localization of seizure onset area from intracranial non-seizure EEG by exploiting locally enhanced synchrony

Spatial correlation based artifact detection for automatic seizure detection in EEG

Automated epilepsy diagnosis using interictal scalp EEG

Automated localization of the seizure focus using interictal intracranial EEG