一种基于随机滤波的神经动作电位信号压缩感知采样方法

神经元细胞通过动作电位进行通信，这些动作电位包含了神经元活动的最关键信息。在论述动作电位在离散小波变换(DWT)域的稀疏特性和分析压缩感知(Compressive Sensing, CS)测量动作电位的基础上，该文提出了一种采用随机滤波对动作电位进行压缩感知测量的方法。从信号恢复和物理可实现性两方面，对比了3种压缩测量的实现方法，从对实测数据的处理结果说明基于随机滤波CS的动作电位测量方案是一种能对动作电位进行压缩，系统复杂度低且最易实现的压缩采样方案。

Compressed Sampling for Neural Action Potentials Based on Random Convolution

Neurons Action Potentials (NAP) contain the most critical information of neurons actions. Based on the discussion for the sparsity characteristics of NAP presented in the DWT domain, and the analysis of related Compressive Sensing (CS) measurements, a compressed sampling method for NAP based on random convolution is proposed. For the aspects of the signal recovery and physical realization, three compression measurement methods are compared. The experiment results show compressed sampling method for NAP based on random convolution is the best compressed sampling scheme of the three for its system realization is simplest.