Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods.
Identifieur interne : 000595 ( Main/Exploration ); précédent : 000594; suivant : 000596Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods.
Auteurs : Chahira Mahjoub [Tunisie] ; Régine Le Bouquin Jeannès [France] ; Tarek Lajnef [Canada] ; Abdennaceur Kachouri [Tunisie]Source :
- Biomedizinische Technik. Biomedical engineering [ 1862-278X ] ; 2020.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Analyse en ondelettes (MeSH), Apprentissage machine (MeSH), Bases de données factuelles (MeSH), Collecte de données (MeSH), Crises épileptiques (diagnostic), Humains (MeSH), Machine à vecteur de support (MeSH), Sensibilité et spécificité (MeSH), Électroencéphalographie (méthodes).
- MESH :
English descriptors
- KwdEn :
- MESH :
- diagnosis : Seizures.
- methods : Electroencephalography.
- Algorithms, Data Collection, Databases, Factual, Humans, Machine Learning, Sensitivity and Specificity, Support Vector Machine, Wavelet Analysis.
Abstract
Electroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.
DOI: 10.1515/bmt-2019-0001
PubMed: 31469648
Affiliations:
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Le document en format XML
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Sfax</wicri:noRegion></affiliation></author><author><name sortKey="Le Bouquin Jeannes, Regine" sort="Le Bouquin Jeannes, Regine" uniqKey="Le Bouquin Jeannes R" first="Régine" last="Le Bouquin Jeannès">Régine Le Bouquin Jeannès</name><affiliation wicri:level="3"><nlm:affiliation>Univ Rennes, INSERM, LTSI-UMR 1099, F-35000 Rennes, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Univ Rennes, INSERM, LTSI-UMR 1099, F-35000 Rennes</wicri:regionArea><placeName><region type="region" nuts="2">Région Bretagne</region><settlement type="city">Rennes</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>Univ Rennes, INSERM, CRIBs, F-35000 Rennes, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>Univ Rennes, INSERM, CRIBs, F-35000 Rennes</wicri:regionArea><placeName><region type="region" nuts="2">Région Bretagne</region><settlement type="city">Rennes</settlement></placeName></affiliation></author><author><name sortKey="Lajnef, Tarek" sort="Lajnef, Tarek" uniqKey="Lajnef T" first="Tarek" last="Lajnef">Tarek Lajnef</name><affiliation wicri:level="1"><nlm:affiliation>Psychology Department, University of Montreal, Montreal, QC, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Psychology Department, University of Montreal, Montreal, QC</wicri:regionArea><wicri:noRegion>QC</wicri:noRegion></affiliation></author><author><name sortKey="Kachouri, Abdennaceur" sort="Kachouri, Abdennaceur" uniqKey="Kachouri A" first="Abdennaceur" last="Kachouri">Abdennaceur Kachouri</name><affiliation wicri:level="1"><nlm:affiliation>LETI-ENIS, University of Sfax, Street of Soukra, 3038 Sfax, Tunisia.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>LETI-ENIS, University of Sfax, Street of Soukra, 3038 Sfax</wicri:regionArea><wicri:noRegion>3038 Sfax</wicri:noRegion></affiliation></author></analytic><series><title level="j">Biomedizinische Technik. Biomedical engineering</title><idno type="eISSN">1862-278X</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Data Collection (MeSH)</term><term>Databases, Factual (MeSH)</term><term>Electroencephalography (methods)</term><term>Humans (MeSH)</term><term>Machine Learning (MeSH)</term><term>Seizures (diagnosis)</term><term>Sensitivity and Specificity (MeSH)</term><term>Support Vector Machine (MeSH)</term><term>Wavelet Analysis (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Analyse en ondelettes (MeSH)</term><term>Apprentissage machine (MeSH)</term><term>Bases de données factuelles (MeSH)</term><term>Collecte de données (MeSH)</term><term>Crises épileptiques (diagnostic)</term><term>Humains (MeSH)</term><term>Machine à vecteur de support (MeSH)</term><term>Sensibilité et spécificité (MeSH)</term><term>Électroencéphalographie (méthodes)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Seizures</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Crises épileptiques</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Electroencephalography</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Électroencéphalographie</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Data Collection</term><term>Databases, Factual</term><term>Humans</term><term>Machine Learning</term><term>Sensitivity and Specificity</term><term>Support Vector Machine</term><term>Wavelet Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse en ondelettes</term><term>Apprentissage machine</term><term>Bases de données factuelles</term><term>Collecte de données</term><term>Humains</term><term>Machine à vecteur de support</term><term>Sensibilité et spécificité</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Electroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.</div></front></TEI><affiliations><list><country><li>Canada</li><li>France</li><li>Tunisie</li></country><region><li>Région Bretagne</li></region><settlement><li>Rennes</li></settlement></list><tree><country name="Tunisie"><noRegion><name sortKey="Mahjoub, Chahira" sort="Mahjoub, Chahira" uniqKey="Mahjoub C" first="Chahira" last="Mahjoub">Chahira Mahjoub</name></noRegion><name sortKey="Kachouri, Abdennaceur" sort="Kachouri, Abdennaceur" uniqKey="Kachouri A" first="Abdennaceur" last="Kachouri">Abdennaceur Kachouri</name></country><country name="France"><region name="Région Bretagne"><name sortKey="Le Bouquin Jeannes, Regine" sort="Le Bouquin Jeannes, Regine" uniqKey="Le Bouquin Jeannes R" first="Régine" last="Le Bouquin Jeannès">Régine Le Bouquin Jeannès</name></region><name sortKey="Le Bouquin Jeannes, Regine" sort="Le Bouquin Jeannes, Regine" uniqKey="Le Bouquin Jeannes R" first="Régine" last="Le Bouquin Jeannès">Régine Le Bouquin Jeannès</name></country><country name="Canada"><noRegion><name sortKey="Lajnef, Tarek" sort="Lajnef, Tarek" uniqKey="Lajnef T" first="Tarek" last="Lajnef">Tarek Lajnef</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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