Titill: | MEEGIPS—A modular EEG investigation and processing system for visual and automated detection of high frequency oscillations |
Höfundur: |
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Útgáfa: | 2019-04-05 |
Tungumál: | Enska |
Háskóli/Stofnun: | Háskólinn á Akureyri University of Akureyri |
Svið: | Hug- og félagsvísindasvið (HA) School of Humanities and Social Sciences (UA) |
Deild: | Sálfræðideild (HA) Faculty of Psychology (UA) |
Birtist í: | Frontiers in Neuroinformatics;13(20) |
ISSN: | 1662-5196 |
DOI: | 10.3389/fninf.2019.00020 |
Efnisorð: | Biomedical Engineering; Neuroscience (miscellaneous); Computer Science Applications; Verkfræði; Taugavísindi; Hugbúnaður; Heilbrigðisvísindi |
URI: | https://hdl.handle.net/20.500.11815/1147 |
Tilvitnun:Höller P, Trinka E and Höller Y (2019) MEEGIPS—A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations. Front. Neuroinform. 13:20. doi: 10.3389/fninf.2019.00020
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Útdráttur:High frequency oscillations (HFOs) are electroencephalographic correlates of brain
activity detectable in a frequency range above 80 Hz. They co-occur with physiological
processes such as saccades, movement execution, and memory formation, but are
also related to pathological processes in patients with epilepsy. Localization of the
seizure onset zone, and, more specifically, of the to-be resected area in patients
with refractory epilepsy seems to be supported by the detection of HFOs. The visual
identification of HFOs is very time consuming with approximately 8 h for 10 min and
20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no
software for visual marking or automated detection of HFOs meets the needs of everyday
clinical practice and research. In the context of the currently available tools and for the
purpose of related local HFO study activities we aimed at converging the advantages
of clinical and experimental systems by designing and developing a comprehensive and
extensible software framework for HFO analysis that, on the one hand, focuses on the
requirements of clinical application and, on the other hand, facilitates the integration
of experimental code and algorithms. The development project included the definition
of use cases, specification of requirements, software design, implementation, and
integration. The work comprised the engineering of component-specific requirements,
component design, as well as component- and integration-tests. A functional and tested
software package is the deliverable of this activity. The project MEEGIPS, a Modular
EEG Investigation and Processing System for visual and automated detection of HFOs,
introduces a highly user friendly software that includes five of the most prominent
automated detection algorithms. Future evaluation of these, as well as implementation
of further algorithms is facilitated by the modular software architecture.
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