FPGA Implementation of a Single Step MFCV Estimator Based on EMG in Diabetic Neuropathy

Daniela De Venuto,Giovanni Mezzina

doi:10.1155/2018/1584068

Abstract

This paper details the design and the hardware implementation of a real-time diagnostic system based on FPGA for the muscle fibre conduction velocity estimation (MFCV). The MFCV is considered as a principal monitoring index for diabetic neuropathy (DPN), as well as in muscle fatigue assessment, to evaluate the muscle fibre status. The FPGA platform evaluates the MFCV during dynamic contractions (e.g., gait), by exploiting a multichannel sensing system composed of 4 wireless surface EMG electrodes, placed in pair on each leg. Raw data are digitized and made binary to create two bitstreams for each monitored limb. Then, a comparison between the two-bit streamed EMGs extracted from the same leg is carried out. The comparison, which allows extracting the MFCV, exploits a computationally light version of the cross-correlation method. The overall architecture implemented and validated on an Altera Cyclone V FPGA is HPS-free and exploits 22.5% ALMs, 10,874 ALUTs, 9.81% registers, 3.36% block memory, and <2.7% of the total wires available on the platform. The choice of FPGA as computing system lies in the possibility to determine resource utilization, related timing constraints for a future real-time ASIC implementation in wearable applications. From the actual muscle contraction during gait (cyclical starting point of the computing), the system spends about 316 ms to acquire useful data and 47.5 ms (on average) to process the signal and provide the output, dynamically dissipating 28.6 mW. The accuracy of the tool evaluation has been evaluated proving the repeatability of the measurements by in vivo test. In this context, 1250 contractions from each subject involved in a protocolled 10-meter walk have been acquired (n=10 subjects evaluated). On average, the same MFCV estimation has been extracted on 1184/1250 contractions (standard deviation of 11 contractions), reaching an accuracy of 94.7%. These estimations fully match the physiological value range reported in literature.

Highlights

Peripheral neuropathy (DPN) is typical of patients with type 2 diabetes [1]
The nerve conduction velocity (NCV) is an indirect measure of the motor unit potential propagation speed on nerve tissue by assessing the electromyographic patterns (EMG) [2]
This section is dedicated to the implementation and testing of the proposed FPGA-based muscle fibre conduction velocity (MFCV) extractor in the contexts of walking assessment

Summary

Introduction

Peripheral neuropathy (DPN) is typical of patients with type 2 diabetes [1]. Clinically, DPN in these patients starts with sole or predominantly sensory dysfunction and disturbance [1]. Several currently in use methods for DPN assessment, such as the nerve conduction velocity (NCV) analysis [2], the laser-evoked potential (LEP) [3], and the SemmesWeinstein monofilament test (SWT) [4], require expensive and cumbersome equipment [3], limiting the subjects’ comfort and movement freedom. The method requires the use of needles as EMG electrodes, to be positioned inside the skin, one to stimulate the muscle under test with an electrical signal (electrostimulation) and another one to collect, in a suitable different point, the induced response. The timing between the transmission and the response collection identify the NCV. This practice excludes the possibility of an auto-positioning of the electrodes

Methods

Results

Conclusion