Enhancing EEG signal analysis with geometry invariants for multichannel fusion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50020818" target="_blank" >RIV/62690094:18470/24:50020818 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.inffus.2023.102023" target="_blank" >https://doi.org/10.1016/j.inffus.2023.102023</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.inffus.2023.102023" target="_blank" >10.1016/j.inffus.2023.102023</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing EEG signal analysis with geometry invariants for multichannel fusion
Original language description
Automated computer-aided diagnosis (CAD) has become an essential approach in the early detection of health issues. One of the significant benefits of this approach is high accuracy and low computational complexity without sacrificing model performance. Electroencephalogram (EEG) signals with seizure detection are one of the critical areas where CAD systems have been developed. In this study, we proposed a CAD system for seizure detection that prioritizes optimizing the solution's complexity. The proposed approach combines geometry invariants multi-channel fusion and amplitude normalization for input data preparation, and experiments on the frequency domain and CNN architecture for reducing complexity. Furthermore, the study includes explainability experiments that should aim to interpret not only the performance of the model but also the analysis of the patterns that contributed to the obtained results. The results demonstrate the effectiveness of the proposed model and its suitability for decision support in both clinical and home environments.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information Fusion
ISSN
1566-2535
e-ISSN
1872-6305
Volume of the periodical
102
Issue of the periodical within the volume
February
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
9
Pages from-to
"Article Number: 102023"
UT code for WoS article
001083197700001
EID of the result in the Scopus database
2-s2.0-85171793659