Skin layer classification by feedforward neural network in bioelectrical impedance spectroscopy.

Abstract

Conductivity change in skin layers has been classified by source indicator o^k (k=1: Stratum corneum, k=2: Epidermis, k=3: Dermis, k=4: Fat, and k=5: Stratum corneum + Epidermis) trained from feedforward neural network (FNN) in bioelectrical impedance spectroscopy (BIS). In BIS studies, treating the skin as a bulk, limits the differentiation of conductivity changes in individual skin layers, however skin layer classification using FNN shows promise in accurately categorizing skin layers, which is essential for predicting source indicators o^k and initiating skin dielectric characteristics diagnosis. The o^k is trained by three main conceptual points which are (i) implementing FNN for predicting k in conductivity change, (ii) profiling four impedance inputs α_ξ consisting of magnitude input α|_z|, phase angle input α_θ, resistance input α_R, and reactance input α_x for filtering nonessential input, and (iii) selecting low and high frequency pair $\left(f_r^{lh}\right)$ by distribution of relaxation time (DRT) for eliminating parasitic noise effect. The training data set of FNN is generated to obtain the α_ξ ∈ R^10×17×10 by 10,200 cases by simulation under configuration and measurement parameters. The trained skin layer classification is validated through experiments with porcine skin under various sodium chloride (NaCl) solutions C_NaCl = {15, 20, 25, 30, 35}[mM] in the dermis layer. FNN successfully classified conductivity change in the dermis layer from experiment with accuracy of 90.6% for the bipolar set-up at $f_6^{lh}=10\&100[\text{kHz]}$ and with the same accuracy for the tetrapolar at $f_8^{lh}=35\&100[\text{kHz]}$. The measurement noise and systematic error in the experimental results are minimized by the proposed method using the feature extraction based on α_ξ at $f_r^{lh}$.