Characterizing the complexity of spontaneous motor unit patterns of amyotrophic lateral sclerosis using approximate entropy

Ping Zhou*, Paul E. Barkhaus, Xu Zhang, William Zev Rymer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


This paper presents a novel application of the approximate entropy (ApEn) measurement for characterizing spontaneous motor unit activity of amyotrophic lateral sclerosis (ALS) patients. High-density surface electromyography (EMG) was used to record spontaneous motor unit activity bilaterally from the thenar muscles of nine ALS subjects. Three distinct patterns of spontaneous motor unit activity (sporadic spikes, tonic spikes and high-frequency repetitive spikes) were observed. For each pattern, complexity was characterized by calculating the ApEn values of the representative signal segments. A sliding window over each segment was also introduced to quantify the dynamic changes in complexity for the different spontaneous motor unit patterns. We found that the ApEn values for the sporadic spikes were the highest, while those of the high-frequency repetitive spikes were the lowest. There is a significant difference in mean ApEn values between two arbitrary groups of the three spontaneous motor unit patterns (P < 0.001). The dynamic ApEn curve from the sliding window analysis is capable of tracking variations in EMG activity, thus providing a vivid, distinctive description for different patterns of spontaneous motor unit action potentials in terms of their complexity. These findings expand the existing knowledge of spontaneous motor unit activity in ALS beyond what was previously obtained using conventional linear methods such as firing rate or inter-spike interval statistics.

Original languageEnglish (US)
Article number066010
JournalJournal of Neural Engineering
Issue number6
StatePublished - Dec 1 2011

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience


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