2020
DOI: 10.1109/tbme.2019.2948397
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On-Line Recursive Decomposition of Intramuscular EMG Signals Using GPU-Implemented Bayesian Filtering

Abstract: Real-time intramuscular electromyography (iEMG) decomposition, which is largely required in the neurological studies and applications, is a complex procedure that involves identifying the motor neuron spike trains from a streaming iEMG recording. We have previously proposed a sequential decomposition algorithm based on a Hidden Markov Model of EMG, that used Bayesian filter to estimate unknown parameters of motor units (MUs) spike trains, as well as their action potentials (MUAPs). In this paper we present a p… Show more

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Cited by 7 publications
(23 citation statements)
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“…and its variance denoted as v S n are also provided by the LMS filter. Details and mathematical derivation of this procedure can be found in [21].…”
Section: A Hidden Markov Modelmentioning
confidence: 99%
See 4 more Smart Citations
“…and its variance denoted as v S n are also provided by the LMS filter. Details and mathematical derivation of this procedure can be found in [21].…”
Section: A Hidden Markov Modelmentioning
confidence: 99%
“…The estimation are mainly based on the last ∞ observations (Y [n − ∞ + 1 : n]) and scenarios (S[n − ∞ + 1 : n]), where ∞ is the maximum window length. More details about the adaptive estimation formulas can be found in [21].…”
Section: A Hidden Markov Modelmentioning
confidence: 99%
See 3 more Smart Citations