Name
Title | ||
|---|---|---|
Detection of multiple sclerosis with visual evoked potentials--an unsupervised computational intelligence system. |
Abstract | ||
|---|---|---|
This paper describes the application of a novel unsupervised pattern recognition system to the classification of the Visual Evoked Potentials (VEP's) of normal and multiple sclerosis (MS) patients. The method combines a traditional statistical feature extractor with a fuzzy clustering method, all implemented in a parallel neural network architecture. The optimization routine, ALOPEX, is used to train the network while decreasing the likelihood of local solutions. The unsupervised system includes a feature extraction and clustering module, trained by the optimization routine ALOPEX. Through maximization of the output variance of each node, and an architecture which excludes redundancy, the feature extraction network retains the most significant Karhunen-Loeve expansion vectors. The clustering module uses a modification to the Fuzzy c-Means (FCM) clustering algorithms, where ALOPEX adjusts a set of cluster centers to minimize an objective error function. The result combines the power of the FCM algorithms with the advantage of a more global solution from ALOPEX. The new pattern recognition system is used to cluster the VEP's of 13 normal and 12 MS subjects. The classification with this technique can, without supervision, separate the patient population into two groups which largely correspond to the MS and control subject groups. A suitable threshold can be chosen so that the recognizer chooses no false negatives. The use of multiple stimulation patterns appears to improve the reliability of the decision. The reasoning of most neural networks in their decision making cannot easily be extracted upon the completion of training. However, due to the linearity of the network nodes, the cluster prototypes of this unsupervised system can be reconstructed to illustrate the reasoning of the system. In this application, this analysis hints at the usefulness of previously unused portions of the VEP in detecting MS. It also indicates a possible use of the system as a training aide. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/4233.870032 | IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society |
Keywords | Field | DocType |
karhunen-loeve transforms,optimization routine,pattern clustering,multiple sclerosis,objective error function,fuzzy clustering method,parallel neural network architecture,minimization,fuzzy c-means,pattern classification,unsupervised computational intelligence system,medical signal processing,statistical feature extractor,visual evoked potentials,fuzzy logic,feature extraction,unsupervised pattern recognition system,classification,medical diagnostic computing,karhunen-loeve expansion vectors,unsupervised learning,patient diagnosis,neural nets,alopex | Fuzzy clustering,Data mining,Population,Computer science,Redundancy (engineering),Unsupervised learning,Artificial intelligence,Artificial neural network,Cluster analysis,Computer vision,Computational intelligence,Pattern recognition,Feature extraction | Journal |
Volume | Issue | ISSN |
4 | 3 | 1089-7771 |
Citations | PageRank | References |
5 | 1.27 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dasey, T.J. | 1 | 5 | 1.61 |
| Evangelia Micheli-Tzanakou | 2 | 14 | 4.50 |