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Comparison of FFT and marginal spectra by Hilbert-Huang transform for broadband spectral analysis of EEG
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  • Eduardo Arrufat-Pié,
  • Mario Estévez-Báez,
  • José Estévez-Carreras,
  • Calixto Machado-Curbelo,
  • Gerry Leisman,
  • Carlos Beltrán
Eduardo Arrufat-Pié
Hospital Comandante Manuel Fajardo
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Mario Estévez-Báez
Instituto de Neurologia y Neurocirugia
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José Estévez-Carreras
Hospital Militar Dr. Luis Díaz Soto
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Calixto Machado-Curbelo
Instituto de Neurologia y Neurocirugia
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Gerry Leisman
University of Haifa
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Carlos Beltrán
Instituto de Neurologia y Neurocirugia
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Abstract

Goal: Fast Fourier transform (FFT), has been the main tool for EEG spectral analysis (SPA). As EEG can show nonlinear and non-stationary behavior, FFT may at times be meaningless. A novel method was developed for analyzing nonlinear and non-stationary signals using the Hilbert-Huang transform. Methods: We compared spectral analyses of EEG using FFT with Hilbert marginal spectra (HMS) with a multivariate empirical mode decomposition algorithm. Segments of continuous 60-sec EEGs recorded from 19 leads of 47 healthy volunteers were studied. Results: HMS showed a reduction of the alpha activity (-5.64%), with increments in the beta-1 (+1.67%), and gamma (+1.38%) fast activity bands, an increment in theta (+2.14%), and in delta (+0.45%) bands, and vice versa for the FFT method. For weighted mean frequencies, insignificant mean differences (lower than 1Hz) were observed between both methods for delta, theta, alpha, beta-1 and beta-2 bands, and only for gamma band values. The HMS were 3 Hz higher than the FFT method. Conclusion: HMS may be considered a good alternative for SPA of the EEG when nonlinearity or non-stationarity may be present.

Peer review status:UNDER REVIEW

04 Aug 2020Submitted to Engineering Reports
04 Aug 2020Assigned to Editor
04 Aug 2020Submission Checks Completed
16 Sep 2020Reviewer(s) Assigned