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On the impact of spike segmentation on motor unit identification in dynamic surface electromyograms | IEEE Conference Publication | IEEE Xplore

On the impact of spike segmentation on motor unit identification in dynamic surface electromyograms


Abstract:

We discuss the adaptation of preexisting Convolution Kernel Compensation (CKC) surface electromyogram (EMG) decomposition technique to dynamic muscle contractions. In par...Show More

Abstract:

We discuss the adaptation of preexisting Convolution Kernel Compensation (CKC) surface electromyogram (EMG) decomposition technique to dynamic muscle contractions. In particular, three different algorithms for segmentation of motor unit (MU) spike trains into MU firings are discussed and mutually compared on synthetic dynamic surface EMG. The first segmentation algorithm employs a priori knowledge of the regularity of MU firings. The second one builds on K-means classification of MU spikes, whereas the third one combines both the regularity of MU firings and the previously introduced Pulse-to-Noise Ratio (PNR). On average, 5.5 ± 0.6 MUs were identified with sensitivity of 88.4 % ± 17.0 %, 83.8 % ± 16.7 % and 90.7 % ± 15.1 % for the first, the second and the third segmentation algorithm, respectively, demonstrating the feasibility of MU identification in moderate dynamic muscle contractions. In our tests, the third segmentation approach demonstrated superior accuracy in MU identification.
Date of Conference: 11-15 July 2017
Date Added to IEEE Xplore: 14 September 2017
ISBN Information:

ISSN Information:

PubMed ID: 29059902
Conference Location: Jeju, Korea (South)

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