Automatic Identification and Classification of Muscle Spasms in Long-Term EMG Recordings

Jeffrey Winslow, Adriana Martinez, Christine K. Thomas

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Spinal cord injured (SCI) individuals may be afflicted by spasticity, a condition in which involuntary muscle spasms are common. EMG recordings can be analyzed to quantify this symptom of spasticity but manual identification and classification of spasms are time consuming. Here, an algorithm was created to find and classify spasm events automatically within 24-h recordings of EMG. The algorithm used expert rules and time-frequency techniques to classify spasm events as tonic, unit, or clonus spasms. A companion graphical user interface (GUI) program was also built to verify and correct the results of the automatic algorithm or manually defined events. Eight channel EMG recordings were made from seven different SCI subjects. The algorithm was able to correctly identify an average (±SD) of 94.5 ± 3.6% spasm events and correctly classify 91.6 ± 1.9% of spasm events, with an accuracy of 61.7 ± 16.2%. The accuracy improved to 85.5 ± 5.9% and the false positive rate decreased to 7.1 ± 7.3%, respectively, if noise events between spasms were removed. On average, the algorithm was more than 11 times faster than manual analysis. Use of both the algorithm and the GUI program provide a powerful tool for characterizing muscle spasms in 24-h EMG recordings, information which is important for clinical management of spasticity.

Original languageEnglish (US)
Article number6807658
Pages (from-to)464-470
Number of pages7
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
StatePublished - Mar 1 2015


  • Automatic classification
  • clonus
  • motor units
  • spasticity
  • spinal cord injury (SCI)

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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