A Model for Epileptic Seizure Diagnosis Using the Combination of Ensemble Learningand Deep Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F24%3A%230001150" target="_blank" >RIV/04274644:_____/24:#0001150 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10670408" target="_blank" >https://ieeexplore.ieee.org/document/10670408</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2024.3457018" target="_blank" >10.1109/ACCESS.2024.3457018</a>
Alternative languages
Result language
angličtina
Original language name
A Model for Epileptic Seizure Diagnosis Using the Combination of Ensemble Learningand Deep Learning
Original language description
Epileptic seizures can be dangerous as they cause sudden and uncontrolled electrical activity in the brain which can lead to injuries if one falls or loss of control over physical functions. To mitigate these risks, machine learning and deep learning algorithms are being developed to anticipate seizure occurrences. Accurate prediction of seizures could enable patients to adopt preventive strategies or initiate medical interventions to halt seizures, thereby minimizing injuries and enhancing safety for individuals afflicted with epilepsy. This paper aims to combine neural networks and Ensemble learning to enhance the accuracy of diagnosing epileptic seizures. By leveraging the strengths of both techniques, the precision in seizure diagnosis can be significantly improved. It also improves the evaluation metrics used in machine learning methodologies for a more comprehensive assessment of diagnostic outcomes. This approach ensures a thorough understanding of the effectiveness of the proposed approach. In this paper, a model with a supreme precision rate is developed to detect epileptic seizures with the help of EEG signals. This study uses an ensemble method, which employs several algorithms, for instance XGB, SVM, RF, and BiLSTM. The used dataset is open access from Bonn University. The proposed methodology reached 98.52% accuracy, 97.37% precision, 95.29% recall, and 96.32% F1-score, respectively.
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
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
12
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
137132-137143
UT code for WoS article
001327294900001
EID of the result in the Scopus database
2-s2.0-85204143969