A4 Article in conference proceedings
An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing (2019)

Yan, R., Li, F., Wang, X., Ristaniemi, T., & Cong, F. (2019). An Automatic Sleep Scoring Toolbox : Multi-modality of Polysomnography Signals’ Processing. In M. Obaidat, C. Callegari, M. van Sinderen, P. Novais, P. Sarigiannidis, S. Battiato, Á. Serrano Sánchez de León, P. Lorenz, & F. Davoli (Eds.), ICETE 2019 : Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS (pp. 301-309). SCITEPRESS Science And Technology Publications. https://doi.org/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:

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

Publication open access: Openly available

Publication channel open access: 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