Generative topographic mapping applied to clustering and visualization of motor unit action potentials
详细信息 查看全文
- 作者：Adriano O. Andrade ; Slawomir Nasuto ; Peter Kyberd and Catherine M. Sweeney-Reed
- 关键词：Cluster analysis ; Electromyography ; Motor unit action potentials ; Generative topographic mapping ; Self-organizing map
- 刊名：Biosystems
- 出版年：2005
- 出版时间：December 2005
- 年：2005
- 卷：82
- 期：3
- 页码：273-284
- 全文大小：734 K
文摘
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).