SVM aided detection of cognitive impairment in MS

Abstract

Cognitive impairment affects half of the multiple sclerosis (MS) patient population, is difficult to detect and requires extensive neuropsychological testing. We analyzed data obtained in a P300 experiment. The P300 is a large positive wave following an unexpected stimulus and is mainly related to attention, a domain frequently impaired in MS. Apart from the traditional features used in P300 experiments we want to investigate the value of different connectivity measures on the classification of MS patients as cognitively intact or impaired. We included 331 MS patients, recruited at the National MS Center Melsbroek (Belgium). About one third was denoted cognitively impaired (104). We divided our patient cohort in a training set (on which we used 10-fold crossvalidation) to optimize the (hyper)parameters of the SVM and an independent test set. Results are reported on this last group to increase the generalizability. In recent years many effort has been devoted to devising connectivity metrics for EEG and MEG data. The most commonly applied metrics are correlation and coherence. However, other metrics have been constructed like the Phase Lag Index (PLI) and the imaginary part of coherency (ImagCoh). Using traditional P300 features, we obtained an accuracy of 68 %. Several connectivity metrics returned similar results, especially the more traditional ones like correlation, correlation in the frequency domain and coherence (delta). The obtained accuracies were, however, only a minor improvement on the accuracy obtained using the traditional P300 features. These results support the recent suggestion that cognitive dysfunction in MS might be caused by cerebral disconnection. We have obtained these results applying graph theoretical analyses on EEG data instead of the more common fMRI network analyses. Although the classification accuracy denotes an important link to cognitive status, it is not sufficient for application in clinical practice.