A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Yan, Rui; Li, Fan; Wang, Xiaoyu; Ristaniemi, Tapani; Cong, Fengyu

Emojulkaisu: ICETE 2019 : Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS

Emojulkaisun toimittajat: Obaidat, Mohammad; Callegari, Christian; van Sinderen, Marten; Novais, Paulo; Sarigiannidis, Panagiotis; Battiato, Sebastiano; Serrano Sánchez de León, Ángel; Lorenz, Pascal; Davoli, Franco

Konferenssi:

Konferenssin paikka ja aika: Prague, Czech Republic, 26.-28.7.2019

ISBN: 978-989-758-378-0

Julkaisuvuosi: 2019

Artikkelin sivunumerot: 301-309

Kirjan kokonaissivumäärä: 386

Kustantaja: SCITEPRESS Science And Technology Publications

Kustannuspaikka: Setúbal

Julkaisumaa: Portugali

Julkaisun kieli: englanti

DOI: https://doi.org/10.5220/0007925503010309

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/65363

Lisätietoja: 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.

Tiivistelmä

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.

YSO-asiasanat: uni (lepotila); signaalianalyysi; MATLAB

Vapaat asiasanat: polysomnography; multi-modality analysis; MATLAB toolbox; automatic sleep scoring

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2019

JUFO-taso: 1