Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section
Lubel, Emma ; Rohlén, Robin LU
; Grandi Sgambato, Bruno
; Barsakcioglu, Deren
; Ibánez, Jaime
; Tang, Meng-Xing
and Farina, Dario
(2023)
In IEEE Transactions on Biomedical Engineering
p.1-1
- Abstract
- Objective: Non-invasive identification of motoneuron (MN) activity commonly uses electromyography (EMG). However, surface EMG (sEMG) detects only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times. Methods: Here,... (More)
- Objective: Non-invasive identification of motoneuron (MN) activity commonly uses electromyography (EMG). However, surface EMG (sEMG) detects only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times. Methods: Here, we present a method based on the convolutive blind source separation of US images to estimate MN discharge times with high accuracy. The method was validated across 10 participants using concomitant sEMG decomposition as the ground truth. Results: 140 unique MN spike trains were identified from US images, with a rate of agreement (RoA) with sEMG decomposition of 87.4 ± 10.3%. Over 50% of these MN spike trains had a RoA greater than 90%. Furthermore, with US, we identified additional MUs well beyond the sEMG detection volume, at up to >30 mm below the skin. Conclusion: The proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images. Significance: The proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/01025b24-4fca-446e-887a-25ea4134db05
- author
- Lubel, Emma
; Rohlén, Robin
LU
; Grandi Sgambato, Bruno
; Barsakcioglu, Deren
; Ibánez, Jaime
; Tang, Meng-Xing
and Farina, Dario
- publishing date
- 2023-12-06
- type
- Contribution to journal
- publication status
- published
- subject
- in
- IEEE Transactions on Biomedical Engineering
- pages
- 12 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- pmid:38055363
- scopus:85179823193
- ISSN
- 1558-2531
- DOI
- 10.1109/TBME.2023.3340019
- language
- English
- LU publication?
- no
- id
- 01025b24-4fca-446e-887a-25ea4134db05
- date added to LUP
- 2023-12-13 09:21:17
- date last changed
- 2024-01-02 04:02:51
@article{01025b24-4fca-446e-887a-25ea4134db05,
abstract = {{Objective: Non-invasive identification of motoneuron (MN) activity commonly uses electromyography (EMG). However, surface EMG (sEMG) detects only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times. Methods: Here, we present a method based on the convolutive blind source separation of US images to estimate MN discharge times with high accuracy. The method was validated across 10 participants using concomitant sEMG decomposition as the ground truth. Results: 140 unique MN spike trains were identified from US images, with a rate of agreement (RoA) with sEMG decomposition of 87.4 ± 10.3%. Over 50% of these MN spike trains had a RoA greater than 90%. Furthermore, with US, we identified additional MUs well beyond the sEMG detection volume, at up to >30 mm below the skin. Conclusion: The proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images. Significance: The proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section.}},
author = {{Lubel, Emma and Rohlén, Robin and Grandi Sgambato, Bruno and Barsakcioglu, Deren and Ibánez, Jaime and Tang, Meng-Xing and Farina, Dario}},
issn = {{1558-2531}},
language = {{eng}},
month = {{12}},
pages = {{1--1}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Biomedical Engineering}},
title = {{Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section}},
url = {{http://dx.doi.org/10.1109/TBME.2023.3340019}},
doi = {{10.1109/TBME.2023.3340019}},
year = {{2023}},
}