Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach

Yang Li, Wei Gang Cui, Hui Huang, Yu Zhu Guo, Ke Li, Tao Tan

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

2 Citaties (Scopus)

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Detecting epileptic seizures in electroencephalography (EEG) signals is a challenging task due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected by clinicians based on visual observation of EEG recordings, which is generally time consuming and sensitive to bias. This paper presents a novel automatic seizure detection method based on the multiscale radial basis function (MRBF) networks and the Fisher vector (FV) encoding. Specifically, the MRBF networks are first used to obtain high-resolution time-frequency (TF) images for feature extraction, where both a modified particle swarm optimization (MPSO) method and an orthogonal least squares (OLS) algorithm are implemented to determine optimal scales and detect a parsimonious model structure. Gray level co-occurrence matrix (GLCM) texture descriptors and the FV, which contribute to high-dimensional vectors, are then adopted to achieve discriminative features based on five frequency subbands of clinical interests from TF images. Furthermore, the dimensionality of the original feature space can be effectively reduced by the t-test statistical tool before feeding compact features into the SVM classifier for seizure detection. Finally, the classification performance of the proposed method is evaluated by using two widely used EEG database, and is observed to provide good classification accuracy on both datasets. Experimental results demonstrate that our proposed method is a powerful tool in detecting epileptic seizures.

TaalEngels
Pagina's96-106
TijdschriftKnowledge-Based Systems
Volume164
DOI's
StatusGepubliceerd - 15 jan 2019

Vingerafdruk

Radial basis function networks
Electroencephalography
Statistical tests
Model structures
Particle swarm optimization (PSO)
Feature extraction
Brain
Classifiers
Textures
Radial basis function

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    @article{885a5b7b910744df96b81111c383387d,
    title = "Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach",
    abstract = "Detecting epileptic seizures in electroencephalography (EEG) signals is a challenging task due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected by clinicians based on visual observation of EEG recordings, which is generally time consuming and sensitive to bias. This paper presents a novel automatic seizure detection method based on the multiscale radial basis function (MRBF) networks and the Fisher vector (FV) encoding. Specifically, the MRBF networks are first used to obtain high-resolution time-frequency (TF) images for feature extraction, where both a modified particle swarm optimization (MPSO) method and an orthogonal least squares (OLS) algorithm are implemented to determine optimal scales and detect a parsimonious model structure. Gray level co-occurrence matrix (GLCM) texture descriptors and the FV, which contribute to high-dimensional vectors, are then adopted to achieve discriminative features based on five frequency subbands of clinical interests from TF images. Furthermore, the dimensionality of the original feature space can be effectively reduced by the t-test statistical tool before feeding compact features into the SVM classifier for seizure detection. Finally, the classification performance of the proposed method is evaluated by using two widely used EEG database, and is observed to provide good classification accuracy on both datasets. Experimental results demonstrate that our proposed method is a powerful tool in detecting epileptic seizures.",
    keywords = "Electroencephalography (EEG), Fisher vector, Modified particle swarm optimization (MPSO), Multiscale radial basis functions (MRBF), Orthogonal least squares (OLS), Seizure detection",
    author = "Yang Li and Cui, {Wei Gang} and Hui Huang and Guo, {Yu Zhu} and Ke Li and Tao Tan",
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    Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach. / Li, Yang; Cui, Wei Gang; Huang, Hui; Guo, Yu Zhu; Li, Ke; Tan, Tao.

    In: Knowledge-Based Systems, Vol. 164, 15.01.2019, blz. 96-106.

    Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

    TY - JOUR

    T1 - Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach

    AU - Li,Yang

    AU - Cui,Wei Gang

    AU - Huang,Hui

    AU - Guo,Yu Zhu

    AU - Li,Ke

    AU - Tan,Tao

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    N2 - Detecting epileptic seizures in electroencephalography (EEG) signals is a challenging task due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected by clinicians based on visual observation of EEG recordings, which is generally time consuming and sensitive to bias. This paper presents a novel automatic seizure detection method based on the multiscale radial basis function (MRBF) networks and the Fisher vector (FV) encoding. Specifically, the MRBF networks are first used to obtain high-resolution time-frequency (TF) images for feature extraction, where both a modified particle swarm optimization (MPSO) method and an orthogonal least squares (OLS) algorithm are implemented to determine optimal scales and detect a parsimonious model structure. Gray level co-occurrence matrix (GLCM) texture descriptors and the FV, which contribute to high-dimensional vectors, are then adopted to achieve discriminative features based on five frequency subbands of clinical interests from TF images. Furthermore, the dimensionality of the original feature space can be effectively reduced by the t-test statistical tool before feeding compact features into the SVM classifier for seizure detection. Finally, the classification performance of the proposed method is evaluated by using two widely used EEG database, and is observed to provide good classification accuracy on both datasets. Experimental results demonstrate that our proposed method is a powerful tool in detecting epileptic seizures.

    AB - Detecting epileptic seizures in electroencephalography (EEG) signals is a challenging task due to nonstationary processes of brain activities. Currently, the epilepsy is mainly detected by clinicians based on visual observation of EEG recordings, which is generally time consuming and sensitive to bias. This paper presents a novel automatic seizure detection method based on the multiscale radial basis function (MRBF) networks and the Fisher vector (FV) encoding. Specifically, the MRBF networks are first used to obtain high-resolution time-frequency (TF) images for feature extraction, where both a modified particle swarm optimization (MPSO) method and an orthogonal least squares (OLS) algorithm are implemented to determine optimal scales and detect a parsimonious model structure. Gray level co-occurrence matrix (GLCM) texture descriptors and the FV, which contribute to high-dimensional vectors, are then adopted to achieve discriminative features based on five frequency subbands of clinical interests from TF images. Furthermore, the dimensionality of the original feature space can be effectively reduced by the t-test statistical tool before feeding compact features into the SVM classifier for seizure detection. Finally, the classification performance of the proposed method is evaluated by using two widely used EEG database, and is observed to provide good classification accuracy on both datasets. Experimental results demonstrate that our proposed method is a powerful tool in detecting epileptic seizures.

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    KW - Fisher vector

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    KW - Multiscale radial basis functions (MRBF)

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