Spike sorting paradigm for classification of multi-channel recorded fasciculation potentials

Faezeh Jahanmiri-Nezhad*, Paul E. Barkhaus, William Zev Rymer, Ping Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Background: Fasciculation potentials (FPs) are important in supporting the electrodiagnosis of Amyotrophic Lateral Sclerosis (ALS). If classified by shape, FPs can also be very informative for laboratory-based neurophysiological investigations of the motor units. Methods: This study describes a Matlab program for classification of FPs recorded by multi-channel surface electromyogram (EMG) electrodes. The program applies Principal Component Analysis on a set of features recorded from all channels. Then, it registers unsupervised and supervised classification algorithms to sort the FP samples. Qualitative and quantitative evaluation of the results is provided for the operator to assess the outcome. The algorithm facilitates manual interactive modification of the results. Classification accuracy can be improved progressively until the user is satisfied. The program makes no assumptions regarding the occurrence times of the action potentials, in keeping with the rather sporadic and irregular nature of FP firings. Results: Ten sets of experimental data recorded from subjects with ALS using a 20-channel surface electrode array were tested. A total of 11891 FPs were detected and classified into a total of 235 prototype template waveforms. Evaluation and correction of classification outcome of such a dataset with over 6000 FPs can be achieved within 1-2 days. Facilitated interactive evaluation and modification could expedite the process of gaining accurate final results. Conclusion: The developed Matlab program is an efficient toolbox for classification of FPs.

Original languageEnglish (US)
Pages (from-to)26-35
Number of pages10
JournalComputers in Biology and Medicine
StatePublished - Dec 1 2014


  • Amyotrophic lateral sclerosis
  • Fasciculation potential
  • Feature extraction
  • Principal component analysis
  • Supervised classification
  • Unsupervised clustering

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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