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GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia

Dan Chen, Duan Li, Muzhou Xiong, Hong Bao, Xiaoli Li
School of Computer Science, University of Birmingham, Edgbaston, Birmingham, U.K.
IEEE Transactions on Information Technology in Biomedicine, Volume 14 Issue 6, November 2010

@article{chen2010gpgpu,

   title={GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia},

   author={Chen, D. and Li, D. and Xiong, M. and Bao, H. and Li, X.},

   journal={Information Technology in Biomedicine, IEEE Transactions on},

   volume={14},

   number={6},

   pages={1417–1427},

   issn={1089-7771},

   year={2010},

   publisher={IEEE}

}

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Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized EEMD method has been developed using general-purpose computing on the graphics processing unit (GPGPU), namely, G-EEMD. A spectral entropy facilitated by G-EEMD was, therefore, proposed to analyze the EEG data for estimating the depth of anesthesia (DoA) in a real-time manner. In terms of EEG data analysis, G-EEMD has dramatically improved the run-time performance by more than 140 times compared to the original serial EEMD implementation. G-EEMD also performs far better than another parallelized implementation of EEMD bases on conventional CPU-based distributed computing technology despite the latter utilizes 16 high-end computing nodes for the same computing task. Furthermore, the results obtained from a pharmacokinetics/pharmacodynamic (PK/PD) model analysis indicate that the EEMD method is slightly more effective than its precedent alternative method (EMD) in estimating DoA, the coefficient of determination R^2 by EEMD is significantly higher than that by EMD (p < 0.05, paired Mest) and the prediction probability Pk by EEMD is also slighter higher than that by EMD (p < 0.2, paired t-test).
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