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Publications

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.

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08.12.2025
METHODS OF AUTOMATIC CLASSIFICATION OF SPONTANEOUS EMG PHENOMENA

Zabrodin K., postgraduate student,
Geletka O., Ph.D., senior physician
Kharkiv National University of Radio Electronics

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05.11.2025
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

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20.09.2025
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.

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10.05.2025
FEATURES OF AMPLIFIERS FOR NEEDLE ELECTROMYOGRAPHY RECORDING

Zabrodin K. Yu., Geletka O. O.,
postgraduate student, candidate of medical sciences, medicine category

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15.12.2024

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