High-precision EMG signal decomposition using communication techniques

Gut, R.; Moschytz, G.S.

doi:10.1109/78.863051

Cited by 48 publications

(30 citation statements)

References 9 publications

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“…In the earlier approaches, the user usually had to manually select and classify the detected MUAPs in order to identify the MUAPt [6], [7], [8]. Later, automatic methods were developed, all based on MUAP waveform classification [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20]. The major limitation of these methods is the difficulty in classifying MUAPs when they appear superimposed in time.…”

Section: Introductionmentioning

confidence: 99%

Decomposition of Multi-Channel Intramuscular EMG Signals by Cyclostationary-Based Blind Source Separation

Roussel

Ravier

Haritopoulos

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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We propose a novel decomposition method for electromyographic (EMG) signals based on blind source separation. Using the cyclostationary properties of motor unit action potential trains (MUAPt), it is shown that MUAPt can be decomposed by joint diagonalization of the cyclic spatial correlation matrix of the observations. After modeling of the source signals, we provide the proof of orthogonality of the sources and of their delayed versions in a cyclostationary context. We tested the proposed method on simulated signals and showed that it can decompose up to 6 sources with a probability of correct detection and classification >95%, using only 8 recording sites. Moreover, we tested the method on experimental multi-channel signals recorded with thinfilm intramuscular electrodes, with a total of 32 recording sites. The rate of agreement of the decomposed MUAPt with those obtained by an expert using a validated tool for decomposition was >93%.Index Terms -EMG, Decomposition, Motor Unit, Firing Rate, Blind Source Separation, Cyclostationary Process, CycloSOBI, convolutive mixture, pulse train separation I IntroductionThe muscle contraction is generated by functional units --the motor units (MU, [1]) --each composed of a motoneuron (MN) innervating a group of muscle fibers. MNs generate electrical impulses, the action potentials (AP), which induce the activation of muscle fibers. The contraction force is controlled by the number of recruited MUs (recruitment) and by the density of APs within the train of each MU (rate coding) [2].The density of APs can be quantified by the inter-spike interval (ISI) or by its inverse value which is referred to as the firing rate (FR). These quantities can be defined in an instantaneous way, or with average values, or through their distributions. The EMG signal comprises the sum of the MUAP trains (MUAPt) of the active MUs. The identification of the MUAPt from the EMG provides the means to identify the neural drive to muscles. This procedure is often termed decomposition of the EMG and has provided fundamental insights into motor unit physiology and neural control of movement [3][4]. In addition to basic neurophysiologic investigations, decomposition of the EMG has also clinical relevance [5]. Acknowledgments The present work is part of the French national project ECOTECH (www.echotechsan.org) which is supported by the French National Agency for research, contract no ANR-12-TECS-0020. The work was also partly funded by the European Research Council (ERC) via the ERC Advanced Grant DEMOVE (No. 267888; to DF). The authors are grateful to Silvia Muceli for providing the experimental data.In this paper, we address the problem of EMG decomposition. Methods for intramuscular EMG decomposition date back to the 70's. In the earlier approaches, the user usually had to manually select and classify the detected MUAPs in order to identify the [34]. The BSS approach has been recently used also for decomposing multi-channel intramuscular EMG signals [35].Here, we propose a new cyclostationary based B...

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Section: Introductionmentioning

confidence: 99%

Decomposition of Multi-Channel Intramuscular EMG Signals by Cyclostationary-Based Blind Source Separation

Roussel

Ravier

Haritopoulos

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…This study aims for the online estimation of the discharge rate of each train, despite these interferences and despite unknown action potential shapes (although a rough initial shape is necessary). It uses some of the concepts proposed in [8] and [9], where the information carried by spike trains is encoded by action potentials waveforms and decoded offline using a Viterbi algorithm. Like [10] and [11], it tackles the online problem, but it also uses tools from reliability theory to handle the regularity of the trains.…”

Section: Introductionmentioning

confidence: 99%

Sequential Decoding of Intramuscular EMG Signals via Estimation of a Markov Model

Monsifrot

Carpentier

Aoustin

et al. 2014

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-This paper addresses the sequential decoding of intramuscular single-channel EMG signals to extract the activity of individual motor neurons. A hidden Markov model is derived from the physiological generation of the EMG signal. The EMG signal is described as a sum of several action potentials (wavelet) trains, embedded in noise. For each train, the time interval between wavelets is modeled by a process that parameters are linked to the muscular activity. The parameters of this process are estimated sequentially by a Bayes filter, along with the firing instants. The method was tested on some simulated signals and an experimental one, from which the rates of detection and classification of action potentials were above 95% with respect to the reference decomposition. The method works sequentially in time, and is the first to address the problem of intramuscular EMG decomposition online. It has potential applications for manmachine interfacing based on motor neuron activities.

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“…The decomposition problem consists of determining the MU label set and the discharge instants that maximize the posterior distribution expressed by (7). The complete search space of the discharge pattern (x i ) i , containing all possible alignments of all possible combinations of MU label sets, is exponential [18]. In this paper, we propose to use the Tabu search algorithm [19] to deal with the maximization w.r.t.…”

Section: Maximization Algorithmmentioning

confidence: 99%

“…First, the EMG signal is segmented and a few representatives of the MUAPs (h (0) i ) i are extracted for initialization of MUAP shapes [see (9)]. It is implemented as a classic preprocessing for MUAP detection in intramuscular recordings and comprises bandpass filtering, thresholding for detection [17], [18], and isolated MUAP shape classification [20]. In this study, we applied a level threshold proportional to the background noise standard deviation estimate σ ǫ .Theactive segments ({Seg k }) are then detected where either isolated or overlapped MUAPs occur.…”

Section: A Overall Structurementioning

confidence: 99%

Unsupervised Bayesian Decomposition of Multiunit EMG Recordings Using Tabu Search

Carpentier

Farina

2010

IEEE Trans. Biomed. Eng.

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Abstract-Intramuscular electromyography (EMG) signals are usually decomposed with semiautomatic procedures that involve the interaction with an expert operator. In this paper, a Bayesian statistical model and a maximum a posteriori (MAP) estimator are used to solve the problem of multiunit EMG decomposition in a fully automatic way. The MAP estimation exploits both the likelihood of the reconstructed EMG signal and some physiological constraints, such as the discharge pattern regularity and the refractory period of muscle fibers, as prior information integrated in a Bayesian framework. A Tabu search is proposed to efficiently tackle the nondeterministic polynomial-time-hard problem of optimization w.r.t the motor unit discharge patterns. The method is fully automatic and was tested on simulated and experimental EMG signals. Compared with the semiautomatic decomposition performed by an expert operator, the proposed method resulted in an accuracy of 90.0% ± 3.8% when decomposing singlechannel intramuscular EMG signals recorded from the abductor digiti minimi muscle at contraction forces of 5% and 10% of the maximal force. The method can also be applied to the automatic identification and classification of spikes from other neural recordings.Index Terms-Bayesian analysis, electromyography (EMG) signal decomposition, Tabu search.

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High-precision EMG signal decomposition using communication techniques

Cited by 48 publications

References 9 publications

Decomposition of Multi-Channel Intramuscular EMG Signals by Cyclostationary-Based Blind Source Separation

Decomposition of Multi-Channel Intramuscular EMG Signals by Cyclostationary-Based Blind Source Separation

Sequential Decoding of Intramuscular EMG Signals via Estimation of a Markov Model

Unsupervised Bayesian Decomposition of Multiunit EMG Recordings Using Tabu Search

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