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Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric.
Open accessAbstract
OBJECTIVE: A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique. APPROACH: The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction. MAIN RESULTS: In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units. SIGNIFICANCE: The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum.
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“The pulse-to-noise ratio, introduced by (29), was used as an indicator of the motor unit identification accuracy (29) and only motor units with pulse-to-noise ratio > 30 dB (corresponding to an accuracy of motor unit firing identification > 90%) were used for further analysis, whereas all other motor units were discarded (29).”
From Modulation of Neural and Muscular Adaptation Processes During Resistance Training by Fish Protein Ingestions in Older Adults (2020) · cites this paper as [29]
In the citing paper
“Only motor units with PNR > 30 dB (corresponding to an accuracy of motor unit firing identification > 90%) were used for further analysis, and all other motor units were discarded (Holobar et al., 2014).”
From Effect of resistance training and fish protein intake on motor unit firing pattern and motor function of elderly (2018) · cites this paper as Holobar et al., 2014
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Metadata
- Authors
- Holobar, A., Minetto, M. A., & Farina, D.
- Affiliations
- University of Maribor (SI)
- University of Turin (IT)
- Bernstein Center for Computational Neuroscience Göttingen (DE)
- Corresponding author
- Holobar, A
- Minetto, M A
- Farina, D
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2014-01-08
- Volume
- 11
- Issue
- 1
- Pages
- 016008
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2042861111
- Cited by
- 400
- References
- 31
- Citations by year
- 2026: 33 · 2025: 56 · 2024: 48 · 2023: 52 · 2022: 41 · 2021: 40 · 2020: 29 · 2019: 28 · 2018: 20 · 2017: 15 · 2016: 16 · 2015: 9 · 2014: 13
- FWCI
- 9.17
- Type
- journal article
- Language
- en
- Open access
- green
- dk.um.si
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- University of Maribor digital library (University of Maribor) (publishedVersion, other-oa)
- GRO (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Health Sciences › Medicine › Clinical Neurology › Peripheral Nerve Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
- Keywords
- motor unit identification, high-density surface electromyography, motor unit firing patterns, signal-to-noise ratio, muscle contraction force, motor unit decomposition
- MeSH terms
- Algorithms, Data Interpretation, Statistical, Electromyography, Humans, Isometric Contraction, Motor Neurons, Muscle Contraction, Signal Processing, Computer-Assisted, Reproducibility of Results, Linear Models, Muscle, Skeletal, Muscle Fibers, Skeletal, Signal-To-Noise Ratio
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