Detection and classification of neural information from peripheral nerves

功能性电刺激（FES）用于恢复脊髓损伤或脑卒中引起的自主运动功能丧失。目前FES系统存在的问题是存在扰动和开环控制引起的肌肉疲劳时恢复效果不佳。为了开发具有自然传感器的闭环FES系统，我们研究了生物相容性和低噪声电极，以及从周围神经提取神经信息的方法。研究了不锈钢SUS316L、Co-Cr基合金NAS604PH和新开发的高氮高锰奥氏体不锈钢NAS 106N的疲劳性能和噪声特性。我们发现每个电极都不会产生多余的噪声，NAS106N在空气和盐水溶液中都比SUS316L和NAS604PH具有更高的疲劳寿命。建立了周围神经模型。我们发现Ranvier’s结与记录电极之间的距离对神经动作电位的记录波形有影响，即使每条纤维的膜直径和性质相似，并且每条纤维在神经干中距离较近，也可以根据记录的波形来识别每条神经纤维的活动。对于自然传感器闭环FES系统的临床应用，记录装置必须放置稳定，对神经纤维的损伤要小。袖口电极满足这些要求。然而，它有时提供的记录信噪比太低，无法检测动作电位。采用小波变换检测低信噪比条件下的动作电位。方法结果可靠。在此基础上，提出了用聚类分析估计单组活动的动作电位分类方法和动作电位分组数的判定标准。结果可靠。

The functional electrical stimulation (FES) is used to restore the loss of voluntary motor function caused by the spinal cord injury or the cerebral apoplexy. Less effective for restoration in the presence of the disturbance and muscle fatigue because of open-loop control system are problems of the current FES system. For developing closedloop FES system with natural sensors, we studied biocompatible and low noise electrodes, and method extraction of neural information from peripheral nerves.Fatigue properties and noise characteristic of stainless steel SUS316L, Co-Cr based alloy NAS604PH and newly developed high nitrogen high manganese austenitic stainless steel NAS 106N were investigated. We found that each electrode did not generate excess noise, and NAS106N had higher fatigue life than SUS316L and NAS604PH in both air and saline solution.A model of peripheral nerve was developed. We found that the distance between Ranvier's node and the recording electrode influenced on the recorded waveforms of nerve action potentials and it was possible to identify activity of each nerve fiber based on the recorded waveforms even if diameter and property of membrane of each fiber were similar and each fiber was close in nerve trunk. For clinical application of closed-loop FES system with natural sensors, the recording device must be placed stably and give minor damage to nerve fiber. Cuff electrode satisfies these requirements. However, it sometimes provides recordings whose signal-to-noise ratio is too low to detect action potentials. We used wavelet transform to detect action potentials under low signal-to-noise ratio condition. Our method provided reliable result. Moreover, we proposed the method for classification of action potentials to estimate single-unit activity with cluster analysis and the criterion for deciding the number of groups that action potentials were classified into. It provided reliable result.