Continuous Riemannian Geometric Learning for Sleep Staging Classification

Seungwoo Jeong; Wonjun Ko; Ahmad Wisnu Mulyadi; Heung Il Suk

doi:10.1109/BCI53720.2022.9734855

Continuous Riemannian Geometric Learning for Sleep Staging Classification

Seungwoo Jeong, Wonjun Ko, Ahmad Wisnu Mulyadi, Heung Il Suk

Department of Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Sleep staging classification has recently received a lot of attention because of its importance and has shown remarkable achievements through deep neural models, but lacks consideration of geometrical structure or continuous time. In this paper, we propose to exploit a diffeomorphism mapping between Riemannian manifolds and a Cholesky space. Further, in order for continuous modeling, we devise a continuous manifold learning method by integrating a manifold ordinary differential equation and a gated recurrent neural network. We demonstrate the validity of our proposed method through experiments using a publicly available SleepEDF-20 dataset.

Original language	English
Title of host publication	10th International Winter Conference on Brain-Computer Interface, BCI 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665413374
DOIs	https://doi.org/10.1109/BCI53720.2022.9734855
Publication status	Published - 2022
Event	10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of Duration: 2022 Feb 21 → 2022 Feb 23

Publication series

Name	International Winter Conference on Brain-Computer Interface, BCI
Volume	2022-February
ISSN (Print)	2572-7672

Conference

Conference	10th International Winter Conference on Brain-Computer Interface, BCI 2022
Country/Territory	Korea, Republic of
City	Gangwon-do
Period	22/2/21 → 22/2/23

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Continuous modeling
Deep neural network
Manifold learning
Sleep staging classification

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Signal Processing

Access to Document

10.1109/BCI53720.2022.9734855

Cite this

Jeong, S., Ko, W., Mulyadi, A. W., & Suk, H. I. (2022). Continuous Riemannian Geometric Learning for Sleep Staging Classification. In 10th International Winter Conference on Brain-Computer Interface, BCI 2022 (International Winter Conference on Brain-Computer Interface, BCI; Vol. 2022-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI53720.2022.9734855

Continuous Riemannian Geometric Learning for Sleep Staging Classification. / Jeong, Seungwoo; Ko, Wonjun; Mulyadi, Ahmad Wisnu et al.
10th International Winter Conference on Brain-Computer Interface, BCI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (International Winter Conference on Brain-Computer Interface, BCI; Vol. 2022-February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jeong, S, Ko, W, Mulyadi, AW & Suk, HI 2022, Continuous Riemannian Geometric Learning for Sleep Staging Classification. in 10th International Winter Conference on Brain-Computer Interface, BCI 2022. International Winter Conference on Brain-Computer Interface, BCI, vol. 2022-February, Institute of Electrical and Electronics Engineers Inc., 10th International Winter Conference on Brain-Computer Interface, BCI 2022, Gangwon-do, Korea, Republic of, 22/2/21. https://doi.org/10.1109/BCI53720.2022.9734855

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abstract = "Sleep staging classification has recently received a lot of attention because of its importance and has shown remarkable achievements through deep neural models, but lacks consideration of geometrical structure or continuous time. In this paper, we propose to exploit a diffeomorphism mapping between Riemannian manifolds and a Cholesky space. Further, in order for continuous modeling, we devise a continuous manifold learning method by integrating a manifold ordinary differential equation and a gated recurrent neural network. We demonstrate the validity of our proposed method through experiments using a publicly available SleepEDF-20 dataset.",

keywords = "Continuous modeling, Deep neural network, Manifold learning, Sleep staging classification",

author = "Seungwoo Jeong and Wonjun Ko and Mulyadi, {Ahmad Wisnu} and Suk, {Heung Il}",

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Continuous Riemannian Geometric Learning for Sleep Staging Classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this