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Outlier detection in high-density surface electromyographic signals

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A Data Descriptor to this article was published on 16 November 2020

Abstract

Recently developed techniques allow the analysis of surface EMG in multiple locations over the skin surface (high-density surface electromyography, HDsEMG). The detected signal includes information from a greater proportion of the muscle of interest than conventional clinical EMG. However, recording with many electrodes simultaneously often implies bad-contacts, which introduce large power-line interference in the corresponding channels, and short-circuits that cause near-zero single differential signals when using gel. Such signals are called ‘outliers’ in data mining. In this work, outlier detection (focusing on bad contacts) is discussed for monopolar HDsEMG signals and a new method is proposed to identify ‘bad’ channels. The overall performance of this method was tested using the agreement rate against three experts’ opinions. Three other outlier detection methods were used for comparison. The training and test sets for such methods were selected from HDsEMG signals recorded in Triceps and Biceps Brachii in the upper arm and Brachioradialis, Anconeus, and Pronator Teres in the forearm. The sensitivity and specificity of this algorithm were, respectively, 96.9 ± 6.2 and 96.4 ± 2.5 in percent in the test set (signals registered with twenty 2D electrode arrays corresponding to a total of 2322 channels), showing that this method is promising.

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Abbreviations

CC:

Correlation coefficient

CPV:

Cumulative percentage variance

EMG:

Electromyography

EP:

Error probability

HDsEMG:

High-density surface electromyographic signals

KDE:

Kernel density estimator

kNN:

k-Nearest neighbors

LDOF:

Local distance-based outlier factor

LOF:

Local outlier factor

MAD:

Median absolute deviation

MCD:

Minimum covariance determinant estimator

MSD:

Mahalanobis squared distance

MVIC:

Maximum voluntary isometric contraction

OCA:

Overall classification accuracy

PC:

Principal component

PCA:

Principal component analysis

PDE:

Partial differential equation

PLOF:

Probabilistic local outlier factor

RMS:

Root mean square

SD (sd):

Standard deviation

Se:

Sensitivity

Sp:

Specificity

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Acknowledgments

We are grateful to Kevin McGill for reviewing a draft of this paper. This work was supported by Compagnia di San Paolo, Fondazione CRT, the Spanish government (TEC2008-02754) and the Doctoral School of Politecnico di Torino, Italy.

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Authors and Affiliations

  1. Laboratory for Engineering of the Neuromuscular Systems, Department of Electronics, Politecnico di Torino, Turin, Italy

    Hamid R. Marateb & Roberto Merletti

  2. Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, CIBER-BBN, CREB, Department ESAII, Technical University of Catalonia, UPC, Barcelona, Spain

    Monica Rojas-Martínez

  3. Department of Biostatistics and Epidemiology, Health School, Isfahan University of Medical Science, Isfahan, Iran

    Marjan Mansourian

  4. Biomedical Engineering Research Centre, CREB, CIBER-BBN, Department ESAII, Technical University of Catalonia, UPC, Barcelona, Spain

    Miguel A. Mañanas Villanueva

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  1. Hamid R. Marateb
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Correspondence to Hamid R. Marateb.

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Marateb, H.R., Rojas-Martínez, M., Mansourian, M. et al. Outlier detection in high-density surface electromyographic signals. Med Biol Eng Comput 50, 79–89 (2012). https://doi.org/10.1007/s11517-011-0790-7

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