Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram.
electrocardiograph interference
quantitative assessment of performance
respiratory monitoring
singular value decomposition
trunk electromyography
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
15 Jan 2021
15 Jan 2021
Historique:
received:
09
12
2020
revised:
04
01
2021
accepted:
12
01
2021
entrez:
20
1
2021
pubmed:
21
1
2021
medline:
27
3
2021
Statut:
epublish
Résumé
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,
Identifiants
pubmed: 33467431
pii: s21020573
doi: 10.3390/s21020573
pmc: PMC7829983
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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