Real-time muscle fiber conduction velocity tracker for diabetic neuropathy monitoring

This paper describes the architecture of a wearable, wireless embedded system for the Diabetic Peripheral Neuropathy (DPN) assessment in ordinary dynamic movements, such as a fluid gait. In this context, the EMG analysis can provide information about the nerves status by estimating the linked Muscle Fiber Conduction Velocity (MFCV). The system operates with synchronized and digitized data samples from 4 EMG channels, which are positioned on each leg of the person under test, exploiting the guidelines provided by an embedded positional scanning algorithm. This work presents a novel algorithm for the estimation of MFCV that is based on the classic 2-electrodes comparative measurement principle. The system uses dynamic thresholds bit-stream conversion of the EMG signals and a low computational solution for the implementation of the bitstream cross-correlator. The entire system fully operates on Altera Cyclone V FPGA. The experimental results on 3 subjects demonstrate the ability of the proposed method for matching the physiological MFCV values, as reported in medical literature. In particular, comparing the medical values, obtained in controlled environments, with the system extracted MFCV, in the same experimental conditions: the absolute error is, on average, 0.2m/s. The system returns a probability of invalid real-time measures below of 4% (worst case).