ArticleSensors (Basel, Switzerland)2021
Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Estimation of Respiratory Effort Through Diaphragmatic Electromyography Features.Sensors (Basel, Switzerland) · 2025Article
- The central nervous system adjusts muscle synergy structure and tightly controls rollator-supported transitions between sitting and standing.Journal of neuroengineering and rehabilitation · 2025Article
- Feasibility Analysis of a Portable Diaphragmatic Efficiency Monitor for CSCI Patients.IEEE journal of translational engineering in health and medicine · 2025Article
- Article
- Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition.Sensors (Basel, Switzerland) · 2022Article
- Spatiotemporal processing in photoplethysmography for skin microcirculatory perfusion imaging.Biomedical optics express · 2022Article
- Coresets for the Average Case Error for Finite Query Sets.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0-20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81,
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