Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of low force voluntary skeletal muscle contractions
Rohlén, Robin LU
; Yu, Jun
and Grönlund, Christer
(2022)
In BMC Research Notes
15(1).
- Abstract
Objective: In this study, the aim was to compare the performance of four spatiotemporal decomposition algorithms (stICA, stJADE, stSOBI, and sPCA) and parameters for identifying single motor units in human skeletal muscle under voluntary isometric contractions in ultrafast ultrasound image sequences as an extension of a previous study. The performance was quantified using two measures: (1) the similarity of components’ temporal characteristics against gold standard needle electromyography recordings and (2) the agreement of detected sets of components between the different algorithms. Results: We found that out of these four algorithms, no algorithm significantly improved the motor unit identification success compared to stICA using... (More)
Objective: In this study, the aim was to compare the performance of four spatiotemporal decomposition algorithms (stICA, stJADE, stSOBI, and sPCA) and parameters for identifying single motor units in human skeletal muscle under voluntary isometric contractions in ultrafast ultrasound image sequences as an extension of a previous study. The performance was quantified using two measures: (1) the similarity of components’ temporal characteristics against gold standard needle electromyography recordings and (2) the agreement of detected sets of components between the different algorithms. Results: We found that out of these four algorithms, no algorithm significantly improved the motor unit identification success compared to stICA using spatial information, which was the best together with stSOBI using either spatial or temporal information. Moreover, there was a strong agreement of detected sets of components between the different algorithms. However, stJADE (using temporal information) provided with complementary successful detections. These results suggest that the choice of decomposition algorithm is not critical, but there may be a methodological improvement potential to detect more motor units.
(Less)
- author
- Rohlén, Robin
LU
; Yu, Jun
and Grönlund, Christer
- publishing date
- 2022-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Blind source separation, Concentric needle electromyography, Decomposition algorithms, Motor units, Ultrafast ultrasound
- in
- BMC Research Notes
- volume
- 15
- issue
- 1
- article number
- 207
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:35705997
- scopus:85132068532
- ISSN
- 1756-0500
- DOI
- 10.1186/s13104-022-06093-1
- language
- English
- LU publication?
- no
- additional info
- Funding Information: Open access funding provided by Umeå University. This work was funded by the Swedish Research Council (Grant Number 2015-04461) and the Kempe foundations (Grant Number JCK-1115). Publisher Copyright: © 2022, The Author(s).
- id
- d1d83eb4-8fb3-4ed9-a6e9-7f840e2eed2f
- date added to LUP
- 2023-05-15 23:58:57
- date last changed
- 2024-04-19 21:47:43
@misc{d1d83eb4-8fb3-4ed9-a6e9-7f840e2eed2f,
abstract = {{<p>Objective: In this study, the aim was to compare the performance of four spatiotemporal decomposition algorithms (stICA, stJADE, stSOBI, and sPCA) and parameters for identifying single motor units in human skeletal muscle under voluntary isometric contractions in ultrafast ultrasound image sequences as an extension of a previous study. The performance was quantified using two measures: (1) the similarity of components’ temporal characteristics against gold standard needle electromyography recordings and (2) the agreement of detected sets of components between the different algorithms. Results: We found that out of these four algorithms, no algorithm significantly improved the motor unit identification success compared to stICA using spatial information, which was the best together with stSOBI using either spatial or temporal information. Moreover, there was a strong agreement of detected sets of components between the different algorithms. However, stJADE (using temporal information) provided with complementary successful detections. These results suggest that the choice of decomposition algorithm is not critical, but there may be a methodological improvement potential to detect more motor units.</p>}},
author = {{Rohlén, Robin and Yu, Jun and Grönlund, Christer}},
issn = {{1756-0500}},
keywords = {{Blind source separation; Concentric needle electromyography; Decomposition algorithms; Motor units; Ultrafast ultrasound}},
language = {{eng}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{BMC Research Notes}},
title = {{Comparison of decomposition algorithms for identification of single motor units in ultrafast ultrasound image sequences of low force voluntary skeletal muscle contractions}},
url = {{http://dx.doi.org/10.1186/s13104-022-06093-1}},
doi = {{10.1186/s13104-022-06093-1}},
volume = {{15}},
year = {{2022}},
}