Enhancing Current Cardiorespiratory-based Approaches of Sleep Stage Classification by Temporal Feature Stacking

Weber L, Gaiduk M, Seepold R, Madrid NM, Glos M, Penzel T (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2021-January

Pages Range: 5518-5522

Conference Proceedings Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Event location: Virtual, Online, MEX

ISBN: 9781728111797

DOI: 10.1109/EMBC46164.2021.9630743

Abstract

This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen's κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.Clinical relevance - The method can be employed generically to feature sets for (small scale) datasets to improve classification performance for classification problems with temporal relations with random forest classifiers.

Authors with CRIS profile

Lucas Weber

Involved external institutions

Hochschule Konstanz University of Applied Sciences (HTWG) DE Germany (DE) I.M. Sechenov First Moscow State Medical University RU Russian Federation (RU) Charité - Universitätsmedizin Berlin DE Germany (DE)

How to cite

APA:

Weber, L., Gaiduk, M., Seepold, R., Madrid, N.M., Glos, M., & Penzel, T. (2021). Enhancing Current Cardiorespiratory-based Approaches of Sleep Stage Classification by Temporal Feature Stacking. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 5518-5522). Virtual, Online, MEX: Institute of Electrical and Electronics Engineers Inc..

MLA:

Weber, Lucas, et al. "Enhancing Current Cardiorespiratory-based Approaches of Sleep Stage Classification by Temporal Feature Stacking." Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Virtual, Online, MEX Institute of Electrical and Electronics Engineers Inc., 2021. 5518-5522.

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