I. Introduction
A large number of nerve fibers constitute the peripheral nerves following particular nerve conduction velocity distribution (CVD) [1]. Applying sufficient electrical stimulus on a motor nerve consisting of motor nerve fibers results in creating the evoked compound muscle action potential (CMAP) that comes from the associated muscles [2]. CMAP is a significant source for exploring the CVD pattern as the information of CVD is encoded in CMAP responses. Since, the conduction velocity, v of a single nerve fiber is known to be related to the diameter of that fiber, [3], the fiber diameter or fiber density distribution (FDD) can be extracted from CVD. CMAP is a linear superposition of motor unit action potential (MUAP) signals, where a motor unit is formed by a single nerve fiber amalgamated with a bunch of muscle fibers. Two very well-studied problems in nerve conduction study are the forward and the inverse problem [4]. The forward problem [4] implies the deduction of unknown CMAP signal from a known CVD or FDD pattern using MUAP model. On the contrary, the inverse problem [4] implies the extraction of CVD or FDD patterns from experimental CMAP data using MUAP model. The determination of FDD or CVD will always be challenging and it has still not been possible to measure this experimentally for a living person. So, in this context, a mathematical model with a very successful estimation rate can help determine this in a non-invasive way.