Modular Framework for Detection of Inter-ictal Spikes in iEEG

Modular Framework for Detection of Inter-ictal Spikes in iEEG

Paper in proceedings (conference paper)

In this paper, we present a new modular approach for detection of inter-ictal spikes in intracranial iEEG recordings from patients that are suffering from pharmaco-resistant form of epilepsy. This new approach is presented in the form of a detection framework consisting of three primary modules: first level detector, second level feature extractor, and third level detection classifier, where each module is responsible for a specific functionality. This detection framework can be perceived as a three slot system, where modules can be easily plugged in their slots and replaced by a different module or implementation on demand, in order to adapt the quality of detection (measured in terms of sensitivity, precision or inter-recording adaptability) and computational cost. Using complex real-world data sets it was confirmed that the proposed framework provides highly sensitive and precise detection, while it also significantly reduces the computation time. 

In this paper, we present a new modular approach for detection of inter-ictal spikes in intracranial iEEG recordings from patients that are suffering from pharmaco-resistant form of epilepsy. This new approach is presented in the form of a detection framework consisting of three primary modules: first level detector, second level feature extractor, and third level detection classifier, where each module is responsible for a specific functionality. This detection framework can be perceived as a three slot system, where modules can be easily plugged in their slots and replaced by a different module or implementation on demand, in order to adapt the quality of detection (measured in terms of sensitivity, precision or inter-recording adaptability) and computational cost. Using complex real-world data sets it was confirmed that the proposed framework provides highly sensitive and precise detection, while it also significantly reduces the computation time. 

Detectors, Electroencephalography, Epilepsy, Feature extraction, Indexes, Sensitivity, Standards

Detectors, Electroencephalography, Epilepsy, Feature extraction, Indexes, Sensitivity, Standards

KEŠNER, F.; SEKANINA, L.; BRÁZDIL, M.

2018

01.02.2017

Institute of Electrical and Electronics Engineers

Los Alamos

978-1-5090-2809-2

The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'17)

418

421

4

https://www.fit.vut.cz/research/publication/11333/

embc17