An algorithm to identify and quantify intermittent (burst-like) muscular patterns of activity in HD-sEMG signals

Alejandra Aranceta-Garza, Roberto Merletti

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Intermittent (burst-like) sEMG activity has been observed in the gastrocnemius muscle during quiet standing [1] and on the erector spinae of sitting violin players. An algorithm based on HD-sEMG detected with 16x8 electrode grid (i.e.d.=10mm, ø=3mm) placed on the erector spinae of a violinist was developed for automatic counting of the bursts. A total of 128 monopolar signals were acquired on each side of the spine at 2048 samples/s and the longitudinal differential signals were computed to reduce power-line and ECG common mode interferences. A moving average window of 60 samples (30ms), with 30 samples overlapping, was applied to the square of the signal to obtain its envelope sampled at 66 samples/s which is 2.5 Nyquist rate. The envelopes were then added along each column of the grid and a threshold of 5% of the peak value of the sum was applied. The resulting binary signal identifies the frequency and duration of the bursts found on the multichannel grid that are greater than noise. The algorithm automatically a) neglects isolated sEMG spikes that are shorter than 20ms or are present on less than 3 channels of a column of the grid, and b) merges two bursts that are separated by less than 65ms. The algorithm was applied to three recordings of 20s duration each, and results were compared with two known methodologies and with the evaluations of 13 human observers. The two methodologies were: 1) Lowpass filtering (5th order bidirectional Butterworth, 5Hz cutoff) of the rectified signal with a threshold based on the background noise to compare and identify patterns of activity greater than noise; 2) Hilbert transform of the raw signal (window size of 30ms) to obtain the analytic signal to extract the envelope with a threshold based on the noise during relaxed conditions [2].
LanguageEnglish
Title of host publicationInternational Society of Electrophysiology and Kinesiology
Subtitle of host publicationAbstracts, Presentations
Place of PublicationDublin
Pages205-205
Number of pages1
Publication statusPublished - 30 Jun 2018
EventInternational Society of Electrophysiology and Kinesiology - University College Dublin, Dublin, Ireland
Duration: 29 Jun 20182 Jul 2018

Conference

ConferenceInternational Society of Electrophysiology and Kinesiology
Abbreviated titleISEK
CountryIreland
CityDublin
Period29/06/182/07/18

Fingerprint

Electrocardiography
Muscle
Electrodes

Keywords

  • sEMG activity
  • muscular patterns

Cite this

Aranceta-Garza, A., & Merletti, R. (2018). An algorithm to identify and quantify intermittent (burst-like) muscular patterns of activity in HD-sEMG signals. In International Society of Electrophysiology and Kinesiology: Abstracts, Presentations (pp. 205-205). Dublin.
Aranceta-Garza, Alejandra ; Merletti, Roberto. / An algorithm to identify and quantify intermittent (burst-like) muscular patterns of activity in HD-sEMG signals. International Society of Electrophysiology and Kinesiology: Abstracts, Presentations. Dublin, 2018. pp. 205-205
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Aranceta-Garza, A & Merletti, R 2018, An algorithm to identify and quantify intermittent (burst-like) muscular patterns of activity in HD-sEMG signals. in International Society of Electrophysiology and Kinesiology: Abstracts, Presentations. Dublin, pp. 205-205, International Society of Electrophysiology and Kinesiology, Dublin, Ireland, 29/06/18.

An algorithm to identify and quantify intermittent (burst-like) muscular patterns of activity in HD-sEMG signals. / Aranceta-Garza, Alejandra; Merletti, Roberto.

International Society of Electrophysiology and Kinesiology: Abstracts, Presentations. Dublin, 2018. p. 205-205.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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AU - Merletti, Roberto

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N2 - Intermittent (burst-like) sEMG activity has been observed in the gastrocnemius muscle during quiet standing [1] and on the erector spinae of sitting violin players. An algorithm based on HD-sEMG detected with 16x8 electrode grid (i.e.d.=10mm, ø=3mm) placed on the erector spinae of a violinist was developed for automatic counting of the bursts. A total of 128 monopolar signals were acquired on each side of the spine at 2048 samples/s and the longitudinal differential signals were computed to reduce power-line and ECG common mode interferences. A moving average window of 60 samples (30ms), with 30 samples overlapping, was applied to the square of the signal to obtain its envelope sampled at 66 samples/s which is 2.5 Nyquist rate. The envelopes were then added along each column of the grid and a threshold of 5% of the peak value of the sum was applied. The resulting binary signal identifies the frequency and duration of the bursts found on the multichannel grid that are greater than noise. The algorithm automatically a) neglects isolated sEMG spikes that are shorter than 20ms or are present on less than 3 channels of a column of the grid, and b) merges two bursts that are separated by less than 65ms. The algorithm was applied to three recordings of 20s duration each, and results were compared with two known methodologies and with the evaluations of 13 human observers. The two methodologies were: 1) Lowpass filtering (5th order bidirectional Butterworth, 5Hz cutoff) of the rectified signal with a threshold based on the background noise to compare and identify patterns of activity greater than noise; 2) Hilbert transform of the raw signal (window size of 30ms) to obtain the analytic signal to extract the envelope with a threshold based on the noise during relaxed conditions [2].

AB - Intermittent (burst-like) sEMG activity has been observed in the gastrocnemius muscle during quiet standing [1] and on the erector spinae of sitting violin players. An algorithm based on HD-sEMG detected with 16x8 electrode grid (i.e.d.=10mm, ø=3mm) placed on the erector spinae of a violinist was developed for automatic counting of the bursts. A total of 128 monopolar signals were acquired on each side of the spine at 2048 samples/s and the longitudinal differential signals were computed to reduce power-line and ECG common mode interferences. A moving average window of 60 samples (30ms), with 30 samples overlapping, was applied to the square of the signal to obtain its envelope sampled at 66 samples/s which is 2.5 Nyquist rate. The envelopes were then added along each column of the grid and a threshold of 5% of the peak value of the sum was applied. The resulting binary signal identifies the frequency and duration of the bursts found on the multichannel grid that are greater than noise. The algorithm automatically a) neglects isolated sEMG spikes that are shorter than 20ms or are present on less than 3 channels of a column of the grid, and b) merges two bursts that are separated by less than 65ms. The algorithm was applied to three recordings of 20s duration each, and results were compared with two known methodologies and with the evaluations of 13 human observers. The two methodologies were: 1) Lowpass filtering (5th order bidirectional Butterworth, 5Hz cutoff) of the rectified signal with a threshold based on the background noise to compare and identify patterns of activity greater than noise; 2) Hilbert transform of the raw signal (window size of 30ms) to obtain the analytic signal to extract the envelope with a threshold based on the noise during relaxed conditions [2].

KW - sEMG activity

KW - muscular patterns

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M3 - Conference contribution book

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BT - International Society of Electrophysiology and Kinesiology

CY - Dublin

ER -

Aranceta-Garza A, Merletti R. An algorithm to identify and quantify intermittent (burst-like) muscular patterns of activity in HD-sEMG signals. In International Society of Electrophysiology and Kinesiology: Abstracts, Presentations. Dublin. 2018. p. 205-205