A modification to the group delay and simulated annealing technique for characterization of peripheral nerve fiber size distributions for non-deterministic sampled data

Abstract

Abstract-The ability to determine the characteristics of peripheral nerve fiber size distributions would provide additional information to clinicians for the diagnosis of specific pathologies of the peripheral nervous system. Investigation of these conditions, using electro-diagnostic techniques, is advantageous in the sense that such techniques tend to be minimally invasive yet provide valuable diagnostic information. One of the principal electro-diagnostic tools available to the clinician is the nerve conduction velocity test. While the peripheral nerve conduction velocity test can provide useful information to the clinician regarding the viability of the nerve under study, it is a single parameter test that yields no detailed information about the characteristics of the functioning nerve fibers within the nerve trunk. In previous work, the efficacy of the group delay and simulated annealing approach was demonstrated in the context of a simulation study where deterministic functions were used to represent the single fiber evoked potentials. In this study we present a modification to the approach discussed previously that is applicable to nondeterministic functions of sampled data.

The Effect of Fiber Depth on the Estimation of Peripheral Nerve Fiber Diameter Using Group Delay and Simulated Annealing Optimization

“…In the previous simulation studies, the activation function of a nerve fiber was only dependent on its diameter [3; 12]. The estimation technique in this study deviates from previous studies by using a different activation function that depends on both the diameter and depth of the nerve fiber [3,12,21]. In addition, the depth of the nerve fiber is no longer constant as in the previous simulation for the purpose of calculating the fiber evoke potential [3; 12].…”

“…The estimated group delay and fiber diameter distribution data are then used in the simulated annealing optimization algorithm to improve the accuracy of the estimation of the fiber diameter distribution [12]. Finding an optimal solution for a problem with large number of possible solutions can be difficult, or impossible, within a reasonable length of time.…”

“…However, because the sample data used in this study is nondeterministic, it is necessary to have a suitable time reference or temporal marker ! that is inherent to sampled data [12]. A relevant temporal marker that can be used is the centroid !…”

“…The functions evaluated in the centroid expression must be greater than zero for all t [12]. The simulated annealing optimization algorithm is implemented according to the flowchart (Figure 7) [12].…”

“…A sample of a complete template distribution of nerve fiber diameters generated using the input parameters in Table 2 in Matlab ..... 11 A more robust method using group delay and simulated annealing optimization that can yield a size distribution of the underlying nerve fibers was first proposed by Szlavik [3,12]. This method uses an estimation of the group delay between two recording electrodes, also known as the phase distortion of the signals travelling down the nerve trunk as a function of the frequency [12].…”