Learning-based Sleep Quality Evaluation

Seungwoo Jeong; Eunjin Jeon; Seungpyo Noh; Jinsool Lee; Hyungjin Kim; Seonguk Kim; Heung Il Suk

doi:10.1109/BCI57258.2023.10078644

Learning-based Sleep Quality Evaluation

Seungwoo Jeong
, Eunjin Jeon
, Seungpyo Noh
, Jinsool Lee
, Hyungjin Kim
, Seonguk Kim
, Heung Il Suk^*

^*Corresponding author for this work

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

Abstract

Analysis of sleep stages is an important issue for understanding optimal sleep environments. However, most studies focus on classifying sleep stages, not on sleep quality. In this work, we develop a framework to evaluate sleep quality by analyzing sleep staging patterns and defining a sleep index for quantification. By exploiting HMMs trained by reference patterns, we compute similarity measures with the structurebased method that is robust to noise. To demonstrate the validity of the proposed method, we conduct experiments using two publicly available MASS and PSG-Audio datasets.

Original language	English
Title of host publication	11th International Winter Conference on Brain-Computer Interface, BCI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665464444
DOIs	https://doi.org/10.1109/BCI57258.2023.10078644
Publication status	Published - 2023
Event	11th International Winter Conference on Brain-Computer Interface, BCI 2023 - Virtual, Online, Korea, Republic of Duration: 2023 Feb 20 → 2023 Feb 22

Publication series

Name	International Winter Conference on Brain-Computer Interface, BCI
Volume	2023-February
ISSN (Print)	2572-7672

Conference

Conference	11th International Winter Conference on Brain-Computer Interface, BCI 2023
Country/Territory	Korea, Republic of
City	Virtual, Online
Period	23/2/20 → 23/2/22

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451; Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning), and in part by LG Electronics (SleepWave Company).

Publisher Copyright:
© 2023 IEEE.

Keywords

Hidden Markov Model
Machine Learning
Similarity Measure
Sleep Staging Analysis

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Signal Processing

Access to Document

10.1109/BCI57258.2023.10078644

Cite this

Jeong, S., Jeon, E., Noh, S., Lee, J., Kim, H., Kim, S., & Suk, H. I. (2023). Learning-based Sleep Quality Evaluation. In 11th International Winter Conference on Brain-Computer Interface, BCI 2023 (International Winter Conference on Brain-Computer Interface, BCI; Vol. 2023-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI57258.2023.10078644

Learning-based Sleep Quality Evaluation. / Jeong, Seungwoo; Jeon, Eunjin; Noh, Seungpyo et al.
11th International Winter Conference on Brain-Computer Interface, BCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (International Winter Conference on Brain-Computer Interface, BCI; Vol. 2023-February).

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

Jeong, S, Jeon, E, Noh, S, Lee, J, Kim, H, Kim, S & Suk, HI 2023, Learning-based Sleep Quality Evaluation. in 11th International Winter Conference on Brain-Computer Interface, BCI 2023. International Winter Conference on Brain-Computer Interface, BCI, vol. 2023-February, Institute of Electrical and Electronics Engineers Inc., 11th International Winter Conference on Brain-Computer Interface, BCI 2023, Virtual, Online, Korea, Republic of, 23/2/20. https://doi.org/10.1109/BCI57258.2023.10078644

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abstract = "Analysis of sleep stages is an important issue for understanding optimal sleep environments. However, most studies focus on classifying sleep stages, not on sleep quality. In this work, we develop a framework to evaluate sleep quality by analyzing sleep staging patterns and defining a sleep index for quantification. By exploiting HMMs trained by reference patterns, we compute similarity measures with the structurebased method that is robust to noise. To demonstrate the validity of the proposed method, we conduct experiments using two publicly available MASS and PSG-Audio datasets.",

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AB - Analysis of sleep stages is an important issue for understanding optimal sleep environments. However, most studies focus on classifying sleep stages, not on sleep quality. In this work, we develop a framework to evaluate sleep quality by analyzing sleep staging patterns and defining a sleep index for quantification. By exploiting HMMs trained by reference patterns, we compute similarity measures with the structurebased method that is robust to noise. To demonstrate the validity of the proposed method, we conduct experiments using two publicly available MASS and PSG-Audio datasets.

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Learning-based Sleep Quality Evaluation

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this