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Deep learning for robust decomposition of high-density surface EMG signals

Clarke, Alexander Kenneth (author)
Imperial College of Science, Technology and Medicine
Atashzar, S. Farokh (author)
New York University
Vecchio, Alessandro Del (author)
Imperial College of Science, Technology and Medicine
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Barsakcioglu, Deren (author)
Imperial College of Science, Technology and Medicine
Muceli, Silvia, 1981 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Bentley, Paul (author)
Imperial College of Science, Technology and Medicine
Urh, Filip (author)
Univerza v Mariboru,University of Maribor
Holobar, Ales (author)
Univerza v Mariboru,University of Maribor
Farina, Dario (author)
Imperial College of Science, Technology and Medicine
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 (creator_code:org_t)
2021
2021
English.
In: IEEE Transactions on Biomedical Engineering. - 0018-9294 .- 1558-2531. ; 68:2, s. 526-534
Related links:
https://doi.org/10.1...
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  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is blindly estimated on a signal interval, it is also possible to apply the same matrix to subsequent signal segments. Nonetheless, the trained separation matrices are sub-optimal in noisy conditions and require that incoming data undergo computationally expensive whitening. One unexplored alternative is to instead use the paired HD-sEMG signal and BSS output to train a model to predict MU activations within a supervised learning framework. A gated recurrent unit (GRU) network was trained to decompose both simulated and experimental unwhitened HD-sEMG signal using the output of the gCKC algorithm. The results on the experimental data were validated by comparison with the decomposition of concurrently recorded intramuscular EMG signals. The GRU network outperformed gCKC at low signal-to-noise ratios, proving superior performance in generalising to new data. Using 12 seconds of experimental data per recording, the GRU performed similarly to gCKC, at rates of agreement of 92.5% (84.5%-97.5%) and 94.9% (88.8%-100.0%) respectively for GRU and gCKC against matched intramuscular sources.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Annan medicinteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Other Medical Engineering (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

neural drive to muscle
Motor unit
deep learning
blind source separation
recurrent neural network

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art (subject category)
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