Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine...

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Published inIEEE access Vol. 11; pp. 564 - 594
Main Authors Miltiadous, Andreas, Tzimourta, Katerina D., Giannakeas, Nikolaos, Tsipouras, Markos G., Glavas, Euripidis, Kalafatakis, Konstantinos, Tzallas, Alexandros T.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial neural networks
detection
EEG
Electroencephalography
Epilepsy
Evaluation
Inspection
Machine learning
Medical research
Recording
Signal classification
Signal processing
signal transformation
Systematic review
Systematics
Transforms
Online AccessGet full text
ISSN2169-3536
2169-3536
DOI10.1109/ACCESS.2022.3232563

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Abstract Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine learning methodologies for automated epilepsy detection. Also, many research or medical facilities have published databases of epileptic EEG signals to accommodate this research effort. The vast number of studies concerning epilepsy detection with EEG makes this systematic review necessary. It presents a detailed evaluation of the signal processing and classification methodologies employed on the different databases and provides valuable insights for future work. 190 studies were included in this systematic review according to the PRISMA guidelines, acquired from a systematic literature search in PubMed, Scopus, ScienceDirect and IEEE Xplore on 1st May 2021. Studies were examined based on the Signal Transformation technique, classification methodology and database for evaluation. Along with other findings, the increasing tendency to employ Convolutional Neural Networks that use a combination of Time-Frequency decomposition methodology images is noticed.
AbstractList Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine learning methodologies for automated epilepsy detection. Also, many research or medical facilities have published databases of epileptic EEG signals to accommodate this research effort. The vast number of studies concerning epilepsy detection with EEG makes this systematic review necessary. It presents a detailed evaluation of the signal processing and classification methodologies employed on the different databases and provides valuable insights for future work. 190 studies were included in this systematic review according to the PRISMA guidelines, acquired from a systematic literature search in PubMed, Scopus, ScienceDirect and IEEE Xplore on 1st May 2021. Studies were examined based on the Signal Transformation technique, classification methodology and database for evaluation. Along with other findings, the increasing tendency to employ Convolutional Neural Networks that use a combination of Time-Frequency decomposition methodology images is noticed.
Author Tsipouras, Markos G.
Glavas, Euripidis
Miltiadous, Andreas
Tzallas, Alexandros T.
Tzimourta, Katerina D.
Kalafatakis, Konstantinos
Giannakeas, Nikolaos
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Snippet Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However,...
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SubjectTerms Algorithms
Artificial neural networks
detection
EEG
Electroencephalography
Epilepsy
Evaluation
Inspection
Machine learning
Medical research
Recording
Signal classification
Signal processing
signal transformation
Systematic review
Systematics
Transforms
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Title Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review
URI https://ieeexplore.ieee.org/document/9999444
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