Abstract
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.
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Acknowledgment
Research of E. Saifutdinova was supported by the project No. SGS17/135/OHK4/2T/13 of the Czech Technical University in Prague. This work was also supported by the project “National Institute of Mental Health (NIMH-CZ)”, grant number ED2.1.00/03.0078 and the European Regional Development Fund and by the project Nr. LO1611 with a financial support from the MEYS under the NPU I program. Research of V. Gerla and L. Lhotska was partially supported by the project “Temporal context in analysis of long-term non-stationary multidimensional signal”, register number 17-20480S of the “Grant Agency of the Czech Republic.”
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Saifutdinova, E., Gerla, V., Lhotská, L. (2017). Riemannian Geometry in Sleep Stage Classification. In: Bursa, M., Holzinger, A., Renda, M., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2017. Lecture Notes in Computer Science(), vol 10443. Springer, Cham. https://doi.org/10.1007/978-3-319-64265-9_8
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DOI: https://doi.org/10.1007/978-3-319-64265-9_8
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