Spike sorting is a typical neural processing technique aimed at identifying the firing activity of individual neurons. It plays a different role in the processing of the signals coming either from a single electrode or an electrode array. In presence of highly noisy recordings, a preliminary denoising stage is required in order to improve the SNR. Despite the significant number of studies in the field, only a few of them deal with peripheral nervous system (PNS) recordings and often the possibility of a real-time implementation is only hinted without any real implementation study. In this paper, a real-time PNS signal processing and classification technique is presented end evaluated on real elec-troneurographic signals taken from the sciatic nerve of rats. A state-of-the-art algorithm, composed of a wavelet denoising preprocessing stage followed by a correlation-based spike sorting and a support vector machine, has been adapted to work on-line in order to improve the processing efficiency while preserving at the most its effectiveness. The algorithm provides some level of adaptiveness with respect to an off-line implementation. On average, the correct classification reach 92.24% with isolated errors that can be easily filtered out. Cycle-accurate profiling results on an off-the-shelf Digital Signal Processor demonstrate the real-time performance.
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