Assessment of Heart Rhythm and Arrhythmia Detection from Electromyographic Signals Using a Convolutional Neural Network.

M A Sazhina, S A Lobov, A S Pimashkin, I V Loskot, V B Kazantsev

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Article in Sovremennye tekhnologii v meditsine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

M A SazhinaResearch Engineer, Engineering Center; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia.

S A LobovDSc, Professor, Neurotechnology Department; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia; Leading Research Fellow, Laboratory of Neurobiomorphic Technologies; Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy Pereulok, Dolgoprudny, Moscow Region, 141701, Russia.

A S PimashkinPhD, Senior Research Fellow, Research Laboratory of Stochastic Multistable Systems; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia.

I V LoskotAssistant, Psychophysiology Department, Faculty of Social Sciences; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia.

V B KazantsevDSc, Head of Neurotechnology Department; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia; Head of the Laboratory of Neurobiomorphic Technologies; Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy Pereulok, Dolgoprudny, Moscow Region, 141701, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Materials and Methods: The study involved 6 healthy male subjects (mean age 21±2 years). Synchronous recording of EMG signals was performed using the EMG system Myosuit, the VNS-Micro electrocardiograph, and the Polar H10 monitor at rest, as well as during static and dynamic bicep contractions. Results: A fully convolutional autoencoder, trained on binary R-wave masks, was developed to extract the corresponding components of the cardiac cycles from the EMG recordings. The model's performance was evaluated using the Conclusion: The feasibility of using the universal EMG electrodes of the EMG system Myosuit for simultaneous monitoring of muscle activity and heart rate has been confirmed. The developed algorithm demonstrates high performance in the task of extracting a rhythmogram from EMG signals both at rest and during muscle load. This opens prospects for creating cost-effective wearable systems for the comprehensive assessment of functional status and real-time screening for cardiac arrhythmias without the need for a separate ECG channel.

Indexed as

Arrhythmias, CardiacElectromyographyHeart RateAdultAlgorithmsAutoencoderConvolutional Neural NetworksElectrocardiographyHumansMaleSignal Processing, Computer-AssistedYoung Adultautoencoderconvolutional neural networkelectrocardiographyelectromyographyheart rate variability

Identifiers

PMID41694721

DOI10.17691/stm2025.17.6.01

PMCPMC12896126

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.