Riemannian Geometry in Sleep Stage Classification

摘要

The study is devoted to the, sleep stage identification problem. Proposed method is based on calculation of covariance matrices from segments of multi-modal recordings. Mathematical properties of the extracted covariance matrices allow to define a distance between two segments - a distance in a Riemannian manifold. In the paper we tested minimum distance to a class center and k-nearest-neighbours classifiers with the Riemannian metric as a distance between two objects, and classification in a tangent space to a Riemannian manifold. Methods were tested on the data of patients suffering from sleep disorders. The maximum obtained accuracy for KNN is 0.94, for minimum distance to a class center it is only 0.816 and for classification in a tangent space is 0.941.