Authors:
Jirí Balach
1
;
Petr Jezdik
1
;
Radek Janca
1
;
Roman Cmejla
1
;
Pavel Krsek
2
;
Petr Marusic
2
and
Premysl Jiruska
3
Affiliations:
1
Czech Technical University in Prague, Czech Republic
;
2
2nd Faculty of Medicine, Charles University in Prague and Motol University Hospital, Czech Republic
;
3
Institute of Physiology and The Czech Academy of Sciences, Czech Republic
Keyword(s):
Intracerebral EEG, High-frequency Oscillations, Circadian Rhythms, Epilepsy, Seizure Onset Zone.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Detection and Identification
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
High frequency oscillations (HFOs) are novel biomarker of epileptogenic tissue. HFOs are currently used to localize the seizure generating areas of the brain, delineate the resection and to monitor the disease activity. It is well established that spatiotemporal dynamics of HFOs can be modified by sleep-wake cycle. In this study we aimed to evaluate in detail circadian and ultradian changes in HFO dynamics using techniques of automatic HFO detection. For this purpose we have developed and implemented novel algorithm to automatic detection and analysis of HFOs in long-term intracranial recordings of six patients. In 5/6 patients HFO rates significantly increased during NREM sleep. The largest NREM related increase in HFO rates were observed in brain areas which spatially overlapped with seizure onset zone. Analysis of long-term recording revealed existence of ultradian changes in HFO dynamics. This study demonstrated reliability of automatic HFO detection in the analysis of long-term
intracranial recordings in humans. Obtained results can foster practical implementation of automatic HFO detecting algorithms into presurgical examination, dramatically decrease human labour and increase the information yield of HFOs.
(More)