Method for concurrent processing of EMG signals from multiple muscles for identification of spasms

F. Sikder, Dilip Sarkar, Odelia Schwartz, Christine K Thomas

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

Abstract

Involuntary contractions are common in muscles paralyzed by spinal cord injury (SCI). These contractions may impact joint movements and upset daily tasks. Existing rule-based algorithms for counting the number of muscle contractions from electromyographic (EMG) signals work on one channel at a time. However, to understand activation of muscles during involuntary contractions, it is important to develop algorithms that can process signals from multiple muscles simultaneously. To characterize these contractions, EMG signals recorded from paralyzed muscles were analyzed. First, existing singlechannel signal processing techniques were applied to each EMG recording. Then an Eigenvalue decomposition technique on a block of signals from all muscles was used to extract features of the signal-block. An extended version of the KL-distance measure (a method to compute the distance between two probability mass distributions) was used to find the distance between two adjacent sets of Eigenvalues. The regions with significant distances were marked as potential areas for identification of muscle contractions. We further developed algorithms to identify co-Activation of muscles and the muscle that was activated first, since this muscle may be targeted in interventions that aim to dampen these contractions. These algorithms were tested on five hours of EMG data (2:00 am to 7:00 am) recorded from five paralyzed (no voluntary control) leg muscles of a person with SCI. Most of the myoclonus spasms that involved contractions containing clonic-like EMG were identified accurately. Although the soleus muscle was often co-Active, on average 74.31% of the time, it initiated the contraction in only 60.91% of the cases. In contrast, the tibialis anterior muscle was co-Activated 37.92% of the time, on average, but was the first muscle to respond 85.48% of the time. While the proposed approach has shown great potential for concurrent analysis of multi-muscle EMG recordings, we want to continue testing on larger data sets and for other spasm types.

Original languageEnglish (US)
Title of host publication2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
Volume2018-January
ISBN (Electronic)9781538648735
DOIs
StatePublished - Jan 12 2018
Event2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Philadelphia, United States
Duration: Dec 2 2017 → …

Other

Other2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017
CountryUnited States
CityPhiladelphia
Period12/2/17 → …

Fingerprint

Spasm
Muscle
Muscles
Processing
Muscle Contraction
Spinal Cord Injuries
Myoclonus
Chemical activation
Smooth Muscle
Leg
Skeletal Muscle
Joints
Signal processing

ASJC Scopus subject areas

  • Health Informatics
  • Clinical Neurology
  • Signal Processing
  • Cardiology and Cardiovascular Medicine

Cite this

Sikder, F., Sarkar, D., Schwartz, O., & Thomas, C. K. (2018). Method for concurrent processing of EMG signals from multiple muscles for identification of spasms. In 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings (Vol. 2018-January, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPMB.2017.8257023

Method for concurrent processing of EMG signals from multiple muscles for identification of spasms. / Sikder, F.; Sarkar, Dilip; Schwartz, Odelia; Thomas, Christine K.

2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.

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

Sikder, F, Sarkar, D, Schwartz, O & Thomas, CK 2018, Method for concurrent processing of EMG signals from multiple muscles for identification of spasms. in 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017, Philadelphia, United States, 12/2/17. https://doi.org/10.1109/SPMB.2017.8257023
Sikder F, Sarkar D, Schwartz O, Thomas CK. Method for concurrent processing of EMG signals from multiple muscles for identification of spasms. In 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6 https://doi.org/10.1109/SPMB.2017.8257023
Sikder, F. ; Sarkar, Dilip ; Schwartz, Odelia ; Thomas, Christine K. / Method for concurrent processing of EMG signals from multiple muscles for identification of spasms. 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6
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abstract = "Involuntary contractions are common in muscles paralyzed by spinal cord injury (SCI). These contractions may impact joint movements and upset daily tasks. Existing rule-based algorithms for counting the number of muscle contractions from electromyographic (EMG) signals work on one channel at a time. However, to understand activation of muscles during involuntary contractions, it is important to develop algorithms that can process signals from multiple muscles simultaneously. To characterize these contractions, EMG signals recorded from paralyzed muscles were analyzed. First, existing singlechannel signal processing techniques were applied to each EMG recording. Then an Eigenvalue decomposition technique on a block of signals from all muscles was used to extract features of the signal-block. An extended version of the KL-distance measure (a method to compute the distance between two probability mass distributions) was used to find the distance between two adjacent sets of Eigenvalues. The regions with significant distances were marked as potential areas for identification of muscle contractions. We further developed algorithms to identify co-Activation of muscles and the muscle that was activated first, since this muscle may be targeted in interventions that aim to dampen these contractions. These algorithms were tested on five hours of EMG data (2:00 am to 7:00 am) recorded from five paralyzed (no voluntary control) leg muscles of a person with SCI. Most of the myoclonus spasms that involved contractions containing clonic-like EMG were identified accurately. Although the soleus muscle was often co-Active, on average 74.31{\%} of the time, it initiated the contraction in only 60.91{\%} of the cases. In contrast, the tibialis anterior muscle was co-Activated 37.92{\%} of the time, on average, but was the first muscle to respond 85.48{\%} of the time. While the proposed approach has shown great potential for concurrent analysis of multi-muscle EMG recordings, we want to continue testing on larger data sets and for other spasm types.",
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