ReviewSensors (Basel, Switzerland)2020
Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed, 29 citations in OpenAlex.
- An Electrospinography Database of Gait-Related Tasks and Motor Imagery Exercises.Scientific data · 2026Article
- Denoising Non-Invasive Electroespinography Signals by Different Cardiac Artifact Removal Algorithms.Biosensors · 2026Article
- Are Reusable Dry Electrodes an Alternative to Gelled Electrodes for Canine Surface Electromyography?Animals : an open access journal from MDPI · 2025Article
- Estimation of Respiratory Effort Through Diaphragmatic Electromyography Features.Sensors (Basel, Switzerland) · 2025Article
- Article
- Electroencephalography Neurofeedback Training with Focus on the State of Attention: An Investigation Using Source Localization and Effective Connectivity.Sensors (Basel, Switzerland) · 2024Article
- A smart approach to EMG envelope extraction and powerful denoising for human-machine interfaces.Scientific reports · 2023Article
- Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review.Sensors (Basel, Switzerland) · 2023Review
- Sensorimotor Time Delay Estimation by EMG Signal Processing in People Living with Spinal Cord Injury.Sensors (Basel, Switzerland) · 2023Article
- Electromyography Parameter Variations with Electrocardiography Noise.Sensors (Basel, Switzerland) · 2022Article
- Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram.Sensors (Basel, Switzerland) · 2021Article
- The application of fNIRS-sEMG in the study of muscle-brain coupling.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.
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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.