dc.contributor.author | Yan, Rui | |
dc.date.accessioned | 2020-10-19T07:38:08Z | |
dc.date.available | 2020-10-19T07:38:08Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-951-39-8329-1 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/72231 | |
dc.description.abstract | 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 | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Jyväskylän yliopisto | |
dc.relation.ispartofseries | JYU dissertations | |
dc.relation.haspart | <b>Artikkeli I:</b> 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. <i>Biomedical Signal Processing and Control, 49, 14-23.</i> <a href="https://doi.org/10.1016/j.bspc.2018.10.001"target="_blank"> DOI: 10.1016/j.bspc.2018.10.001</a> | |
dc.relation.haspart | <b>Artikkeli II:</b> Yan, Rui; Li, Fan; Wang, Xiaoyu; Ristaniemi, Tapani; Cong, Fengyu (2019). An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing. In <i>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.</i> <a href="https://doi.org/10.5220/0007925503010309"target="_blank"> DOI: 10.5220/0007925503010309</a> | |
dc.relation.haspart | <b>Artikkeli III:</b> Yan, Rui; Li, Fan; Wang, Xiaoyu; Ristaniemi, Tapani; Cong, Fengyu (2020). Automatic Sleep Scoring Toolbox and Its Application in Sleep Apnea. In <i>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.</i> <a href="https://doi.org/10.1007/978-3-030-52686-3_11"target="_blank"> DOI: 10.1007/978-3-030-52686-3_11</a> | |
dc.relation.haspart | <b>Artikkeli IV:</b> 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 <i>28th European Signal Processing Conference (EUSIPCO 2020). 5 pages. IEEE, Amsterdam, Netherlands.</i> | |
dc.relation.haspart | <b>Artikkeli V:</b> Rui Yan, Fan Li, Dongdong Zhou, Tapani Ristaniemi, and Fengyu Cong. 2020. Automatic Sleep Scoring: A Deep Learning Architecture for Multi-modality Time Series. <i>Submitted to the Journal of Neuroscience Methods.</i> | |
dc.rights | In Copyright | |
dc.subject | aivotutkimus | |
dc.subject | uni (lepotila) | |
dc.subject | unihäiriöt | |
dc.subject | signaalianalyysi | |
dc.subject | signaalinkäsittely | |
dc.subject | koneoppiminen | |
dc.subject | automatic sleep scoring | |
dc.subject | polysomnography | |
dc.subject | multi-modality analysis | |
dc.subject | deap learning | |
dc.subject | machine learning | |
dc.title | Automatic sleep scoring based on multi-modality polysomnography data | |
dc.type | Diss. | |
dc.identifier.urn | URN:ISBN:978-951-39-8329-1 | |
dc.contributor.yliopisto | University of Jyväskylä | en |
dc.contributor.yliopisto | Jyväskylän yliopisto | fi |
dc.relation.issn | 2489-9003 | |
dc.rights.copyright | © The Author & University of Jyväskylä | |
dc.rights.accesslevel | openAccess | |
dc.type.publication | doctoralThesis | |
dc.format.content | fulltext | |
dc.rights.url | https://rightsstatements.org/page/InC/1.0/ | |