Karim Meddah, Hadjer Zairi, Besma Bessekri, Hachemi Cherrih, M. Kedir-Talha
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引用次数: 2
摘要
本研究旨在利用离散小波分解(DWT)和主成分分析(PCA)建立癫痫发作的FPGA设计模型,确定支持向量机(svm)对脑电分类数据的最优参数。本文介绍了FPGA的硬件实现。首先,开发了一种优化的基于软件的医学诊断方法,仅使用每个DWT水平计算的方差来确定EEG类别。这种特征提取的优化导致FPGA原型尺寸的减小和能耗的节约。其次,采用Xilinx System Generator (XSG)作为DSP,在Nexys 4 Artix 7板上设计并实现了该方法。通过两项比较研究对所提出的系统进行了性能评价,第一项是将浮点的Matlab结果与定点的XSG结果进行对比研究。将基于FPGA定点实现的分类性能与基于MATLAB浮点实现的分类性能进行了比较。第二次比较是将所得的性能与文献中现有作品的性能进行比较。
FPGA implementation of Epileptic Seizure detection based on DWT, PCA and Support Vector Machine
The study aims to establish an FPGA design model for epileptic seizures with discrete wavelet decomposition (DWT) and principal component analysis (PCA) to determine the optimum parameters of support vector machine (SVMs) for the EEG classification data. The FPGA Hardware implementation is described in this paper. Firstly, an optimized software-based medical diagnostic approach has been developed to determine the EEG class using only the variance calculated for each DWT level. This features extracted optimization leads to reduce the FPGA prototype size and to save energy consumption. Secondly, the proposed method has been designed and implemented on the Nexys 4 Artix 7 board using the Xilinx System Generator (XSG) for DSP. The performance evaluation of the proposed system has been made through two comparative studies, the first one, between the floating-point Matlab results and the fixed-point XSG results. The classification performances obtained from the proposed FPGA fixed-point implementation were compared to those obtained from the MATLAB floating-point. The second comparison was performed between the resulting performances and those obtained with the existing work in literature.