Classification of biomedical signals for differential diagnosis of Raynaud's phenomenon

Abstract

This paper discusses a supervised classification approach for the differential diagnosis of Raynaud's phenomenon (RP). The classification of data from healthy subjects and from patients suffering for primary and secondary RP is obtained by means of a set of classifiers derived within the framework of linear discriminant analysis. A set of functional variables and shape measures extracted from rewarming/reperfusion curves are proposed as discriminant features. Since the prediction of group membership is based on a large number of these features, the high dimension/small sample size problem is considered to overcome the singularity problem of the within-group covariance matrix. Results on a data set of 72 subjects demonstrate that a satisfactory classification of the subjects can be achieved through the proposed methodology.

Keywords:

• functional infrared imaging
• biomedical time series
• linear discriminant analysis
• feature extraction and selection
• sparse methods
• high-dimensional data

Acknowledgements

We are grateful to the reviewers for their useful comments. The authors would also like to thank Nickolay Trendafilov and Line Clemmensen for invaluable comments and discussions on preliminary versions of the paper.