Imperial College London
Portrait Picture

ProfessorDarioFarina

Faculty of Engineering, Department of Bioengineering

Chair in Neurorehabilitation Engineering
 
 
 
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Contact

 

+44 (0)20 7594 1387d.farina Website

 
 
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Location

 

RSM 4.15Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Marateb:2016:10.1371/journal.pone.0167954,
author = {Marateb, HR and Farahi, M and Rojas, M and Mañanas, MA and Farina, D},
doi = {10.1371/journal.pone.0167954},
journal = {PLOS One},
title = {Detection of multiple innervation zones from multi-channel surface emg recordings with low signal-to-noise ratio using graph-cut segmentation},
url = {http://dx.doi.org/10.1371/journal.pone.0167954},
volume = {11},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was perfor
AU  - Marateb,HR
AU  - Farahi,M
AU  - Rojas,M
AU  - Mañanas,MA
AU  - Farina,D
DO  - 10.1371/journal.pone.0167954
PY  - 2016///
SN  - 1932-6203
TI  - Detection of multiple innervation zones from multi-channel surface emg recordings with low signal-to-noise ratio using graph-cut segmentation
T2  - PLOS One
UR  - http://dx.doi.org/10.1371/journal.pone.0167954
UR  - http://hdl.handle.net/10044/1/44481
VL  - 11
ER  -
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