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.

Diabetic Retinopathy Analysis

“…Visual evoked potential (VEP) has been used in the clinical environment as a diagnostic tool for a long time [20, 21, 22]. VEP is one of the noninvasive tools in analyzing diabetic retinopathy [23, 24, 25].…”

“…In general, the clinical use of VEP is based on the peak amplitude and the latencies of the N75, P100, and N145 [22, 35, 36, 37]. The amplitude and the latencies of these peaks are measured directly from the signal [38, 39].…”

A Supplementary System for a Brain-Machine Interface Based on Jaw Artifacts for the Bidimensional Control of a Robotic Arm

“…Spontaneous systems are usually focused on generating commands to control a device taking advantage of the users capability to control their EEG signals [5] – [7] . Regarding evoked systems, there are studies focused on generating control commands [8] , [9] and also on the evaluation of the brain response to different external stimulus with diagnosis purposes [10] – [12] . Besides, BMIs (both spontaneous and evoked) are used on other topics in the field of human health, such as the measurement of the mental state of a patient (workload, attention level, emotional state,...) [13] or as support systems on rehabilitation processes [14] .…”

Development of Gradient Descent Adaptive Algorithms to Remove Common Mode Artifact for Improvement of Cardiovascular Signal Quality

“…ALOPEX has been used previously for rapid and stable convergence in a variety of settings including combinatorial optimization, 19 pattern matching, 1 fuzzy clustering of auditory neuronal responses, 4 detection of multiple sclerosis with visual evoked potentials, 15 and blood cell identification using neural networks. 20 When multiple reference inputs are used for image processing, parallel processing elements can be incorporated, one for each input, to speed convergence.…”