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A reconfigurable parallel acceleration platform for evaluation of permutation entropy.

Xiaowei Ren, Pengju Ren, Badong Chen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study accelerates permutation entropy calculation for EEG time series using a parallel FPGA platform. This enables faster real-time analysis for predicting brain diseases like epileptic seizures.

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    Area of Science:

    • Biomedical Engineering
    • Computational Neuroscience
    • Signal Processing

    Background:

    • Permutation entropy is crucial for analyzing electroencephalogram (EEG) complexity and predicting neurological disorders like epileptic seizures.
    • Analyzing numerous EEG time series simultaneously is computationally intensive, hindering real-time applications.

    Purpose of the Study:

    • To develop a hardware-accelerated method for computing permutation entropy in EEG time series.
    • To enable efficient and real-time analysis of complex EEG data for clinical applications.

    Main Methods:

    • Designed and implemented a parallel Field-Programmable Gate Array (FPGA) platform with 128 reconfigurable pipelines.
    • Utilized the platform to calculate permutation entropy for individual EEG time series.

    Main Results:

    • Achieved an average speedup of 5553 compared to C code on a multi-core CPU.
    • Demonstrated high-performance computation at 150MHz with an embedding dimension of 5.
    • Maintained low hardware costs.

    Conclusions:

    • The parallel FPGA platform significantly accelerates permutation entropy computation for EEG analysis.
    • This acceleration facilitates real-time prediction of brain diseases, offering a practical solution for large-scale EEG data processing.