NDLI: Detection of Cognitive Impairment in MS Based on an EEG P300 Paradigm

Content Provider	IEEE Xplore Digital Library
Author	Van Schependom, J. D'hooge, M. Cleynhens, K. D'hooghe, M.B. de Keyser, J. Nagels, G.
Copyright Year	2013
Description	Author affiliation: Center for Neurosciences, Vrije Univ. Brussel, Brussels, Belgium (Van Schependom, J.; D'hooge, M.; Cleynhens, K.; D'hooghe, M.B.; de Keyser, J.; Nagels, G.)
Abstract	Cognitive impairment affects half of the multiple sclerosis (MS) patient population and is an important factor of quality of life. Cognitive impairment is, however, difficult to detect. Apart from the traditional features used in P300 experiments (e.g. amplitude and latency at different electrodes), we want to investigate the value of network-features on the classification of MS patients as cognitively intact or impaired. We included 305 MS patients, recruited at the National MS Center Melsbroek (Belgium). About half of them was denoted cognitively impaired (143). We divided this patient group in a training set (on which we used 10-fold cross validation) and an independent test set. Results are reported on this last group to increase the generalizability. We found the correlations linking electrodes from one hemisphere with the other significantly different between the two groups MS patients. Especially in the parietal region this difference was very significant (1.5E-12). Using a simple cutoff on this variable, lead to a Percentage Correctly Classified (PCC) of 0.70 and an Area Under Curve (AUC) of the Receiver Operator Curve (ROC) of 0.76. The network parameters that were calculated showed a comparable result for the total number of edges included in the network. Combining these features in a logistic regression model, artificial neural networks or Naive Bayes resulted in a PCC's of 0.68-0.70. These results support the recent suggestion that cognitive dysfunction in MS is caused by a disconnection mechanism in the cerebellum. We have obtained these results applying graph theoretical analyses on EEG data instead of the more common fMRI-analyses. The classification accuracy obtained is, however, not yet sufficient for application in clinical practice.
Starting Page	114
Ending Page	118
File Size	375484
Page Count	5
File Format	PDF
ISBN	9780769550619
DOI	10.1109/PRNI.2013.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Multiple Sclerosis Correlation Multiple sclerosis Pattern Recognition EEG Feature extraction Electroencephalography Batteries Network Analysis Logistics
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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