Enhancing EEG signal analysis with geometry invariants for multichannel fusion

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing EEG signal analysis with geometry invariants for multichannel fusion

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

Enhancing EEG signal analysis with geometry invariants for multichannel fusion

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information Fusion

ISSN

1566-2535

e-ISSN

1872-6305

Svazek periodika

102

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

"Article Number: 102023"

Kód UT WoS článku

001083197700001

EID výsledku v databázi Scopus

2-s2.0-85171793659