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癫痫是神经系统常见疾病之一,患病率仅次于脑卒中。最近的研究表明,脑电波(EEG)信号中一定的高频振荡(HFOs)跟癫痫的发生有相似的特征,然而传统的EEG分析算法由于需要计算庞大的向量数据而花费很长的时间。本文提出了基于FPGA的硬件架构可以极大的提高识别癫痫的分析能力。基于FPGA的实验结果表明,我们的系统可以实时的处理频率为110MHz的多信道EEG信号。
Epilepsy is one of the common diseases of nervous system, the prevalence is second only to stroke. Recent studies have shown that certain high frequency oscillations (HFOs) in EEG signals have similar characteristics to epilepsy, whereas traditional EEG analysis algorithms take a long time due to the need to compute large vector data. This paper presents a FPGA-based hardware architecture that can greatly improve the ability to identify epilepsy. FPGA-based experimental results show that our system can handle real-time multi-channel EEG signals at 110MHz.