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. The model was trained on real biomedical data containing various types of noise and artifacts. Results show that the proposed model achieves high
filtering accuracy with minimal deviation from the original signals (a mean amplitude deviation of only −0.04%, and a 20.75% increase in signal-to-noise ratio, SNR). The CNN-based approach demonstrated strong potential for preserving diagnostically significant features and can be effectively
integrated into clinical EMG workflows to enhance diagnostic precision and reduce the impact of external
interference.
FEATURES OF AMPLIFIERS FOR NEEDLE ELECTROMYOGRAPHY RECORDING
Zabrodin K. Yu., Geletka O. O.,
postgraduate student, candidate of medical sciences, medicine category
MAIN CHARACTERISTICS OF MOTOR UNIT POTENTIALS RECORDED WITH A CONCENTRIC NEEDLE ELECTRODE
Zabrodin K. Yu., Geletka O. O.,
postgraduate student,
Candidate of Medical Sciences, Doctor of the Highest Category
METHODS OF STIMULATION ELECTRONEUROMYOGRAPHY
Abstract
This document discusses the main purpose of stimulation electroneuromyography. The importance of conducting such an examination for a wide range of pathologies is emphasized. The main criteria for prescribing this examination to patients of different age groups are given. The main methods of stimulation EMG, as well as the parameters of the device settings during the examination, are considered.
METHODS FOR AUTOMATIC LABELING OF MOTOR UNIT POTENTIALS RECORDEDWITH THE USE OF A CONCENTRIC NEEDLE ELECTRODE
Abstract
The article reviews the main methods for determining motor action unit potential (MUAP) duration in electromyography. Various algorithms, such as the Turku 1 method, the Turku 2 method, the
Stolberg method, and the Nandedkar method, which are used to determine the MUP limits are analyzed. The advantages and disadvantages of each approach are described, as well as their practical applicability in clinical and scientific electromyography.
