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• W4306643400 endingPage "104274" @default.
• W4306643400 startingPage "104274" @default.
• W4306643400 abstract "• The SSTFT is used to map the EEG into time–frequency domain. • We introduce GNMF to make a quadratic interpretation of TFR. • The FKNN is then adopted to recognize the EEG patterns based on the LogE features. • The obtained results confirm the transfer ability of the proposed features. The EEG is an indispensable tool for clinical diagnosis of neurological disorders. The issue that how to correctly decoding the pathological information from EEG has been attracting a lot of interest. This paper creates a robust feature extraction scheme based on synchrosqueezing short-time Fourier transform (SSTFT) and graph regularized non-negative matrix factorization (GNMF), which is applied to epileptic EEG detection. The SSTFT is employed to analyze the EEG in time–frequency domain, followed by a GNMF decomposition that maps these components into a low-dimensional representation where latent clues are easily emphasized and learned, and finally the logarithmic energy, computed as features, is fed to a fuzzy K-Nearest Neighbor classifier for the purpose of discriminating seizure events. Our method is evaluated using two public databases including Bonn database and CHB-MIT database. Obtained results confirm the superiority of the proposed features by achieving classification accuracy of at least 98.0% for both short-term and long-term EEG recordings. It is expected to offer a new thought and perspective for the study and design of the automatic EEG monitoring system." @default.
• W4306643400 created "2022-10-18" @default.
• W4306643400 creator A5006051351 @default.
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• W4306643400 date "2023-02-01" @default.
• W4306643400 modified "2023-10-13" @default.
• W4306643400 title "GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection" @default.
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• W4306643400 doi "https://doi.org/10.1016/j.bspc.2022.104274" @default.
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