title: Spike sorting paradigm for classification of multi-channel recorded fasciculation potentials.
abstract: 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.
  
  
  CONCLUSIONS
  The developed Matlab program is an efficient toolbox for classification of FPs.
authors.affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA. Electronic address: fjahan3@uic.edu.
authors.forename: Faezeh
authors.initials: F
authors.lastname: Jahanmiri-Nezhad
authors.affiliation: Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA.
authors.forename: Paul E
authors.initials: PE
authors.lastname: Barkhaus
authors.affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.
authors.forename: William Zev
authors.initials: WZ
authors.lastname: Rymer
authors.affiliation: Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, and TIRR Memorial Hermann Research Center, Houston, TX, USA; Biomedical Engineering Program, University of Science and Technology of China, Hefei, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Zhou
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000368:Aged; D000690:Amyotrophic Lateral Sclerosis / Q000503:physiopathology; D016000:Cluster Analysis; D004576:Electromyography / Q000379:methods*; D005207:Fasciculation / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000503:physiopathology; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted
keywords: Amyotrophic lateral sclerosis; Fasciculation potential; Feature extraction; Principal component analysis; Supervised classification; Unsupervised clustering