Generative topographic mapping applied to clustering and visualization of motor unit action potentials.
The identification and visualization of clusters formed by motor unit action potentials (MUAPs) is an essential step in investigations seeking to explain the control of the neuromuscular system. This work introduces the generative topographic mapping (GTM), a novel machine learning tool, for clustering of MUAPs, and also it extends the GTM technique to provide a way of visualizing MUAPs. The performance of GTM was compared to that of three other clustering methods: the self-organizing map (SOM), a Gaussian mixture model (GMM), and the neural-gas network (NGN). The results, based on the study of experimental MUAPs, showed that the rate of success of both GTM and SOM outperformed that of GMM and NGN, and also that GTM may in practice be used as a principled alternative to the SOM in the study of MUAPs. A visualization tool, which we called GTM grid, was devised for visualization of MUAPs lying in a high-dimensional space. The visualization provided by the GTM grid was compared to that obtained from principal component analysis (PCA).
Andrade
Adriano O
AO
The University of Reading, Whiteknights, Reading, Berkshire RG6 6AY, UK. a.d.o.andrade@rdg.ac.uk
Nasuto
Slawomir
S
Kyberd
Peter
P
Sweeney-Reed
Catherine M
CM
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D002874:Chromosome Mapping; D016000:Cluster Analysis; D019295:Computational Biology; D004576:Electromyography; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009132:Muscles / Q000378:metabolism; D009474:Neurons / Q000378:metabolism; D016011:Normal Distribution; D049490:Systems Biology; D013997:Time Factors