Iskanje po katalogu digitalne knjižnice

Ključne besede: M wave, high-density surface EMG, firing identification, motor unit
Objavljeno v DKUM: 13.06.2024; Ogledov: 144; Prenosov: 19
Celotno besedilo (2,30 MB)
Gradivo ima več datotek! Več...

Opis: We analyzed the capability of previously introduced Convolution Kernel Compensation (CKC) method to identify clusters of motor units (MUs) that share similar motor unit action potentials (MUAPs) and, therefore, cannot be mutually discriminated by the decomposition of high-density surface electromyograms (hdEMG). The tests were performed on biceps brachii muscle because hdEMG decomposition yields relatively small number of individual MUs in this muscle. In this study, we analyzed how many MUs of biceps brachii get merged into the same spike train by the CKC method due to MUAP similarity and what are the sensitivity and precision of MU discharge identification in merged spike trains. We compared these metrics with the identification of individual MUs in both synthetic and experimental hdEMG. In synthetic hdEMG with 20 dB noise, the number of identified MUs increased from 5.2 ± 2.8 (individual MUs) to 16.4±8.4 MUs when merged MU spike trains were taken into consideration, in addition to individual MUs. Discharges of individual MUs were identified with sensitivity of 77.0±15.8 % and precision of 86.1±25.4 %, whereas the merg ed MUs were identified with sensitivity of 79.6±15.7 % and precision of 97.4±11.8 %. Similar results were observed also for noiseless hdEMG. In experimental hdEMG signals from biceps brachii muscle of two young healthy individuals, the number of identified MUs increased from 7.5±2.4 (individual MUs) to 21.9±17.6 MUs (merged MU spike trains). Individual MUs were identified with sensitivity of 83.3±15.9 % and precision of 80.0±19.0 %, whereas when considering also merged MUs, the sensitivity and precision of MU discharge identification increased to 82.3±22.4 % and 94.9±11.4 %, respectively. In conclusion, the merged MU spike trains obtained by hdEMG decompositions carry important information about the activity of skeletal muscles and can be used to increase the number of MUs identified from hdEMG.
Ključne besede: površinski elektromiogram (EMG), motorična enota, vlak impulzov, zlivanje motoričnih enot, biceps brachii
Objavljeno v DKUM: 30.05.2024; Ogledov: 175; Prenosov: 12
Celotno besedilo (1,01 MB)

Opis: Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6 ± 4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85 ± 1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield.
Ključne besede: EMG, electromyograms, muscle, convultions
Objavljeno v DKUM: 15.06.2017; Ogledov: 2868; Prenosov: 421
Celotno besedilo (1,18 MB)
Gradivo ima več datotek! Več...

Opis: This study addresses online decomposition of high-density surface electromyograms (EMG) in real-time. The proposed method is based on previouslypublished Convolution Kernel Compensation (CKC) technique and sharesthe same decomposition paradigm, i.e. compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires ~ 10 s of EMG signal, the real-time implementation begins with batch processing of ~ 3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal Linear Minimum Mean Square Error (LMMSE) estimator demonstrates high agreementin identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98 %. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90 % of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.
Ključne besede: discharge pattern, high-density EMG, surface EMG, motor unit, real time decomposition
Objavljeno v DKUM: 25.05.2015; Ogledov: 1639; Prenosov: 0

Opis: Targeted muscle reinnervation (TMR) redirects nerves that have lost their target, due to amputation, to remaining muscles in the region of the stump with the intent of establishing intuitive myosignals to control a complex prosthetic device. In order to directly recover the neural code underlying an attempted limb movement, in this paper, we present the decomposition of high-density surface electromyographic (EMG) signals detected from three TMR patients into the individual motor unit spike trains. The aim was to prove, for the first time, the feasibility of decoding the neural drive that would reach muscles of the missing limb in TMR patients, to show the accuracy of the decoding, and to demonstrate the representativeness of the pool of extracted motor units. Six to seven flexible EMG electrode grids of 64 electrodes each were mounted over the reinnervated muscles of each patient, resulting in up to 448 EMG signals. The subjects were asked to attempt elbow extension and flexion, hand open and close, wrist extension and flexion, wrist pronation and supination, of their missing limb. The EMG signals were decomposed using the Convolution Kernel Compensation technique and the decomposition accuracy was evaluated with a signal-based index of accuracy, called pulse-to-noise ratio (PNR). The results showed that the spike trains of 3 to 27 motor units could be identified for each task, with a sensitivity of the decomposition > 90%, as revealed by PNR. The motor unit discharge rates were within physiological values of normally innervated muscles. Moreover, the detected motor units showed a high degree of common drive so that the set of extracted units per task was representative of the behavior of the population of active units. The results open a path for a new generation of human-machine interfaces in which the control signals are extracted from noninvasive recordings and the obtained neural information is based directly on the spike trains of motor neurons.
Ključne besede: electromyographic, EMG, decomposition, high-density EMG, motor neuron, motor unit, myoelectronic control, neural drive to muscle, target muscle reinervation, TMR
Objavljeno v DKUM: 25.05.2015; Ogledov: 1630; Prenosov: 0