Alternating diffusion for common manifold learning with application to sleep stage assessment

Published

Conference Paper

© 2015 IEEE. In this paper, we address the problem of multimodal signal processing and present a manifold learning method to extract the common source of variability from multiple measurements. This method is based on alternating-diffusion and is particularly adapted to time series. We show that the common source of variability is extracted from multiple sensors as if it were the only source of variability, extracted by a standard manifold learning method from a single sensor, without the influence of the sensor-specific variables. In addition, we present application to sleep stage assessment. We demonstrate that, indeed, through alternating-diffusion, the sleep information hidden inside multimodal respiratory signals can be better captured compared to single-modal methods.

Full Text

Duke Authors

Cited Authors

  • Lederman, RR; Talmon, R; Wu, HT; Lo, YL; Coifman, RR

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 2015-August /

Start / End Page

  • 5758 - 5762

International Standard Serial Number (ISSN)

  • 1520-6149

International Standard Book Number 13 (ISBN-13)

  • 9781467369978

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2015.7179075

Citation Source

  • Scopus