Automatic sleep scoring based on multi-modality polysomnography data
Julkaistu sarjassa
JYU dissertationsTekijät
Päivämäärä
2020Tekijänoikeudet
© The Author & University of Jyväskylä
Over the past decades, probably due to our hectic lifestyle in modern society,
complaints about sleep problems have increased dramatically, affecting a large
part of the world’s population. The polysomnography (PSG) test is a common
tool for diagnosing sleep problems, but the scoring of PSG recordings is an
essential but time-consuming process. Therefore, automatic sleep scoring
becomes crucial and urgent to settle the growing unmet needs in sleep research.
This thesis extends the previous research on automatic sleep scoring from
two aspects. One is to extensively explore signal modalities and feature types
related to automatic sleep scoring. This exploratory work obtains the optimal
signal fusion and feature set for automatic sleep scoring, and further clarifies the
contribution of signals and features to the discrimination of sleep stages. Our
results demonstrate that diverse features and signal modalities are coordinative
and complementary, which benefits the improvement of classification accuracy.
The other one is to develop automatic sleep scoring tools that can accommodate
different datasets and sample populations without adjusting model structure and
parameters across tasks. Experimental results show that the joint analysis of
multiple signals can improve the stability, robustness and generalizability of the
proposed models. Model performance has been verified on multiple public
datasets, demonstrating good model transferability between different datasets
and diverse disease populations.
In summary, this research finding will advance the understanding of
underlying mechanism during automatic sleep scoring and clarify the
association between manual scoring criteria and automatic scoring methods. The
joint analysis of multiple signals enhances model versatility, which inspires the
construction of cross-model in the field of automatic sleep scoring. Moreover, the
proposed automatic sleep scoring methods can be integrated with diverse PSG
systems, thereby facilitating sleep monitoring in clinical or routine care.
Keywords: automatic sleep scoring, polysomnography, multi-modality analysis,
deep learning, machine learning
...
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Julkaisija
Jyväskylän yliopistoISBN
978-951-39-8329-1ISSN Hae Julkaisufoorumista
2489-9003Julkaisuun sisältyy osajulkaisuja
- Artikkeli I: Yan, R., Zhang, C., Spruyt, K., Wei, L., Wang, Z., Tian, L., . . . Cong, F. (2019). Multi-modality of polysomnography signals’ fusion for automatic sleep scoring. Biomedical Signal Processing and Control, 49, 14-23. DOI: 10.1016/j.bspc.2018.10.001
- Artikkeli II: Yan, Rui; Li, Fan; Wang, Xiaoyu; Ristaniemi, Tapani; Cong, Fengyu (2019). An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing. In Obaidat, Mohammad; Callegari, Christian; van Sinderen, Marten; Novais, Paulo; Sarigiannidis, Panagiotis; Battiato, Sebastiano; Serrano Sánchez de León, Ángel; Lorenz, Pascal; Davoli, Franco (Eds.) ICETE 2019 : Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS. Setúbal: SCITEPRESS Science And Technology Publications, 301-309. DOI: 10.5220/0007925503010309
- Artikkeli III: Yan, Rui; Li, Fan; Wang, Xiaoyu; Ristaniemi, Tapani; Cong, Fengyu (2020). Automatic Sleep Scoring Toolbox and Its Application in Sleep Apnea. In Obaidat, Mohammad S. (Eds.) ICETE 2019 : 16th International Joint Conference on e-Business and Telecommunications, Revised Selected Papers, Communications in Computer and Information Science, 1247. Cham: Springer, 256-275. DOI: 10.1007/978-3-030-52686-3_11
- Artikkeli IV: Rui Yan, Fan Li, DongDong Zhou, Tapani Ristaniemi and Fengyu Cong. (2020). A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series. In 28th European Signal Processing Conference (EUSIPCO 2020). 5 pages. IEEE, Amsterdam, Netherlands.
- Artikkeli V: Rui Yan, Fan Li, Dongdong Zhou, Tapani Ristaniemi, and Fengyu Cong. 2020. Automatic Sleep Scoring: A Deep Learning Architecture for Multi-modality Time Series. Submitted to the Journal of Neuroscience Methods.
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A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
Yan, Rui; Li, Fan; Zhou, DongDong; Ristaniemi, Tapani; Cong, Fengyu (IEEE, 2020)Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to ... -
Automatic sleep scoring : a deep learning architecture for multi-modality time series
Yan, Rui; Li, Fan; Zhou, Dong Dong; Ristaniemi, Tapani; Cong, Fengyu (Elsevier, 2021)Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This ... -
Multi-modality of polysomnography signals’ fusion for automatic sleep scoring
Yan, Rui; Zhang, Chi; Spruyt, Karen; Wei, Lai; Wang, Zhiqiang; Tian, Lili; Li, Xueqiao; Ristaniemi, Tapani; Zhang, Jihui; Cong, Fengyu (Elsevier BV, 2019)Objective: The study aims to develop an automatic sleep scoring method by fusing different polysomnography (PSG) signals and further to investigate PSG signals’ contribution to the scoring result. Methods: Eight combinations ... -
An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing
Yan, Rui; Li, Fan; Wang, Xiaoyu; Ristaniemi, Tapani; Cong, Fengyu (SCITEPRESS Science And Technology Publications, 2019)Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. To speed up the process of sleep scoring without compromising accuracy, this paper develops an automatic sleep scoring toolbox with the ... -
SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG
Zhou, Dongdong; Wang, Jian; Hu, Guoqiang; Zhang, Jiacheng; Li, Fan; Yan, Rui; Kettunen, Lauri; Chang, Zheng; Xu, Qi; Cong, Fengyu (Elsevier, 2022)In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) ...
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