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• W2053153607 abstract "In this paper, we present a new technique for automatic seizure detection in electroencephalogram (EEG) signals by using Hilbert marginal spectrum (HMS) analysis. As the EEG signal is highly nonlinear and nonstationary, the traditional Fourier analysis which expands signals in terms of sinusoids cannot appropriately represent the amplitude contribution from each frequency value. The HMS is derived from the empirical mode decomposition (EMD) which decomposes signal into a collection of intrinsic mode functions (IMFs). Since this decomposition is based on the local characteristic time scale of the signal, it can be well applied to nonlinear and nonstationary processes. In this work, the spectral entropies and energy features of frequency-bands of the rhythms using HMS analysis are extracted and fed into the support vector machine (SVM) for seizure detection of EEG signals. A final comparison between the results obtained with the developed technique and results adopted by Polat and coworkers using Fourier analysis with the same database is given to show the effectiveness of this technique for seizure detection." @default.
• W2053153607 created "2016-06-24" @default.
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• W2053153607 date "2015-04-01" @default.
• W2053153607 modified "2023-10-06" @default.
• W2053153607 title "Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals" @default.
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• W2053153607 doi "https://doi.org/10.1016/j.bspc.2015.01.002" @default.
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