EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern
An analytical derivation of the EMG signal’s amplitude probability density function (EMG PDF) is presented and used to study how an EMG signal builds-up, or fills, as the degree of muscle contraction increases. The EMG PDF is found to change from a semi-degenerate distribution to a Laplac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10034779/ |
| _version_ | 1797805071039201280 |
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| author | Javier Navallas Adrian Eciolaza Cristina Mariscal Armando Malanda Javier Rodriguez-Falces |
| author_facet | Javier Navallas Adrian Eciolaza Cristina Mariscal Armando Malanda Javier Rodriguez-Falces |
| author_sort | Javier Navallas |
| collection | DOAJ |
| description | An analytical derivation of the EMG signal’s amplitude probability density function (EMG PDF) is presented and used to study how an EMG signal builds-up, or fills, as the degree of muscle contraction increases. The EMG PDF is found to change from a semi-degenerate distribution to a Laplacian-like distribution and finally to a Gaussian-like distribution. We present a measure, the EMG filling factor, to quantify the degree to which an EMG signal has been built-up. This factor is calculated from the ratio of two non-central moments of the rectified EMG signal. The curve of the EMG filling factor as a function of the mean rectified amplitude shows a progressive and mostly linear increase during early recruitment, and saturation is observed when the EMG signal distribution becomes approximately Gaussian. Having presented the analytical tools used to derive the EMG PDF, we demonstrate the usefulness of the EMG filling factor and curve in studies with both simulated signals and real signals obtained from the tibialis anterior muscle of 10 subjects. Both simulated and real EMG filling curves start within the 0.2 to 0.35 range and rapidly rise towards 0.5 (Laplacian) before stabilizing at around 0.637 (Gaussian). Filling curves for the real signals consistently followed this pattern (100% repeatability within trials in 100% of the subjects). The theory of EMG signal filling derived in this work provides (a) an analytically consistent derivation of the EMG PDF as a function of motor unit potentials and motor unit firing patterns; (b) an explanation of the change in the EMG PDF according to degree of muscle contraction; and (c) a way (the EMG filling factor) to quantify the degree to which an EMG signal has been built-up. |
| first_indexed | 2024-03-13T05:46:26Z |
| format | Article |
| id | doaj.art-27b43071f73944d48e79cbfb4749baf7 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:26Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-27b43071f73944d48e79cbfb4749baf72023-06-13T20:09:35ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01311188119810.1109/TNSRE.2023.324135410034779EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference PatternJavier Navallas0https://orcid.org/0000-0002-9582-0022Adrian Eciolaza1https://orcid.org/0000-0002-5486-1661Cristina Mariscal2https://orcid.org/0000-0003-0808-6833Armando Malanda3https://orcid.org/0000-0002-3122-9049Javier Rodriguez-Falces4https://orcid.org/0000-0002-9150-8955Department of Electrical, Electronic and Communications Engineering, Public University of Navarra (UPNA), Pamplona, SpainDepartment of Electrical, Electronic and Communications Engineering, Public University of Navarra (UPNA), Pamplona, SpainDepartment of Clinical Neurophysiology, Hospital Universitario de Navarra, Pamplona, SpainDepartment of Electrical, Electronic and Communications Engineering, Public University of Navarra (UPNA), Pamplona, SpainDepartment of Electrical, Electronic and Communications Engineering, Public University of Navarra (UPNA), Pamplona, SpainAn analytical derivation of the EMG signal’s amplitude probability density function (EMG PDF) is presented and used to study how an EMG signal builds-up, or fills, as the degree of muscle contraction increases. The EMG PDF is found to change from a semi-degenerate distribution to a Laplacian-like distribution and finally to a Gaussian-like distribution. We present a measure, the EMG filling factor, to quantify the degree to which an EMG signal has been built-up. This factor is calculated from the ratio of two non-central moments of the rectified EMG signal. The curve of the EMG filling factor as a function of the mean rectified amplitude shows a progressive and mostly linear increase during early recruitment, and saturation is observed when the EMG signal distribution becomes approximately Gaussian. Having presented the analytical tools used to derive the EMG PDF, we demonstrate the usefulness of the EMG filling factor and curve in studies with both simulated signals and real signals obtained from the tibialis anterior muscle of 10 subjects. Both simulated and real EMG filling curves start within the 0.2 to 0.35 range and rapidly rise towards 0.5 (Laplacian) before stabilizing at around 0.637 (Gaussian). Filling curves for the real signals consistently followed this pattern (100% repeatability within trials in 100% of the subjects). The theory of EMG signal filling derived in this work provides (a) an analytically consistent derivation of the EMG PDF as a function of motor unit potentials and motor unit firing patterns; (b) an explanation of the change in the EMG PDF according to degree of muscle contraction; and (c) a way (the EMG filling factor) to quantify the degree to which an EMG signal has been built-up.https://ieeexplore.ieee.org/document/10034779/Electromyography (EMG)EMG PDFmotor unitinterference patternrecruitment |
| spellingShingle | Javier Navallas Adrian Eciolaza Cristina Mariscal Armando Malanda Javier Rodriguez-Falces EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern IEEE Transactions on Neural Systems and Rehabilitation Engineering Electromyography (EMG) EMG PDF motor unit interference pattern recruitment |
| title | EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern |
| title_full | EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern |
| title_fullStr | EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern |
| title_full_unstemmed | EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern |
| title_short | EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern |
| title_sort | emg probability density function a new way to look at emg signal filling from single motor unit potential to full interference pattern |
| topic | Electromyography (EMG) EMG PDF motor unit interference pattern recruitment |
| url | https://ieeexplore.ieee.org/document/10034779/ |
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