Artifact Correction Method for M-response usingUNet1D Network
Abstract—this article explores the application of convolutional neural networks (CNNs), specifically the
UNet1D architecture, for filtering M-response signals − an essential component of electromyographic (EMG) diagnostics used to assess the functional state of the
peripheral neuromuscular system. The goal of the study is to improve signal quality by effectively removing artifacts while
preserving the amplitude-frequency characteristics critical for clinical interpretation.
