A4 Article in conference proceedings
An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing (2019)
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
JYU authors or editors
Publication details
All authors or editors: Yan, Rui; Li, Fan; Wang, Xiaoyu; Ristaniemi, Tapani; Cong, Fengyu
Parent publication: ICETE 2019 : Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS
Parent publication editors: Obaidat, Mohammad; Callegari, Christian; van Sinderen, Marten; Novais, Paulo; Sarigiannidis, Panagiotis; Battiato, Sebastiano; Serrano Sánchez de León, Ángel; Lorenz, Pascal; Davoli, Franco
Conference:
- International Conference on Signal Processing and Multimedia Applications
Place and date of conference: Prague, Czech Republic, 26.-28.7.2019
ISBN: 978-989-758-378-0
Publication year: 2019
Pages range: 301-309
Number of pages in the book: 386
Publisher: SCITEPRESS Science And Technology Publications
Place of Publication: Setúbal
Publication country: Portugal
Publication language: English
DOI: https://doi.org/10.5220/0007925503010309
Open Access: Publication published in an open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65363
Additional information: Article presented at the SIGMAP 2019 conference, the 16th International Conference on Signal Processing and Multimedia Applications conference, as part of ICETE, the Joint Conference on e-Business and Telecommunications.
Abstract
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 capability of multi-signal processing. It allows the user to choose signal types and the number of target classes. Then, an automatic process containing signal pre-processing, feature extraction, classifier training (or prediction) and result correction will be performed. Finally, the application interface displays predicted sleep structure, related sleep parameters and the sleep quality index for reference. To improve the identification accuracy of minority stages, a layer-wise classification strategy is proposed according to the signal characteristics of sleep stages. The context of the current stage is taken into consideration in the correction phase by employing a Hidden Markov Model to study the transition rules of sleep stages in the training dataset. These transition rules will be used for logic classification results. The performance of proposed toolbox has been tested on 100 subjects with an average accuracy of 85.76%. The proposed automatic scoring toolbox would alleviate the burden of the physicians, speed up sleep scoring, and expedite sleep research.
Keywords: sleep; signal analysis; MATLAB
Free keywords: polysomnography; multi-modality analysis; MATLAB toolbox; automatic sleep scoring
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2019
JUFO rating: 1