title: A reconfigurable parallel acceleration platform for evaluation of permutation entropy.
abstract: In recent years, permutation entropy is widely used to characterize the complexity of EEG time series and can be applied to predict the onset of serious brain diseases, such as the epileptic seizure. In many practical situations, the number of EEG time series that need to be analyzed simultaneously is very large, so the computation of the permutation entropy is time-consuming and should be accelerated so that the real-time analysis is possible. Noting that mathematical operations can be sped up effectively with hardware implementation, we design a parallel FPGA platform consisting of 128 reconfigurable pipelines, which are used to calculate the permutation entropy for a single EEG time series. When the platform works at 150MHz and the embedding dimension is 5, an average speedup of 5553 for different window sizes is achieved compared with C codes running on a 3GHz Intel(R) Core(TM) i5-2320 CPU. Meanwhile, the hardware cost is very low.
authors.lastname: Xiaowei Ren
authors.lastname: Pengju Ren
authors.lastname: Badong Chen
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
authors.lastname: Nanning Zheng
mesh_terms: D004569:Electroencephalography; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis; D005069:Evaluation Studies as Topic; D006801:Humans; D012815:Signal Processing, Computer-Assisted