A4 Artikkeli konferenssijulkaisussa
Automatic Sleep Scoring Toolbox and Its Application in Sleep Apnea (2020)
Yan, R., Li, F., Wang, X., Ristaniemi, T., & Cong, F. (2020). Automatic Sleep Scoring Toolbox and Its Application in Sleep Apnea. In M. S. Obaidat (Ed.), ICETE 2019 : 16th International Joint Conference on e-Business and Telecommunications, Revised Selected Papers (pp. 256-275). Springer. Communications in Computer and Information Science, 1247. https://doi.org/10.1007/978-3-030-52686-3_11
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Yan, Rui; Li, Fan; Wang, Xiaoyu; Ristaniemi, Tapani; Cong, Fengyu
Emojulkaisu: ICETE 2019 : 16th International Joint Conference on e-Business and Telecommunications, Revised Selected Papers
Emojulkaisun toimittajat: Obaidat, Mohammad S.
Konferenssi:
- International Joint Conference on e-Business and Telecommunications
Konferenssin paikka ja aika: Prague, Czech Republic, 26.-28.7.2019
ISBN: 978-3-030-52685-6
eISBN: 978-3-030-52686-3
Lehti tai sarja: Communications in Computer and Information Science
ISSN: 1865-0929
eISSN: 1865-0937
Julkaisuvuosi: 2020
Sarjan numero: 1247
Artikkelin sivunumerot: 256-275
Kirjan kokonaissivumäärä: 277
Kustantaja: Springer
Kustannuspaikka: Cham
Julkaisumaa: Sveitsi
Julkaisun kieli: englanti
DOI: https://doi.org/10.1007/978-3-030-52686-3_11
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Lisätietoja: Extended and revised version of a paper presented at ICETE 2019, the 16th International Joint Conference on e-Business and Telecommunications.
Tiivistelmä
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 needs for sleep research. Therefore, this paper aims to develop an automatic sleep scoring toolbox with the capability of multi-signal processing. The toolbox allows the user to choose signal types and the number of target classes. In addition, a user-friendly interface is provided to display sleep structures and related sleep parameters. The proposed approach employs several automatic processes including signal preprocessing, feature extraction and classification in order to save labor costs without compromising accuracy. For the phase of feature extraction, a huge number of features are considered including statistical characters, frequency characters, time-frequency characters, fractal characters, entropy characters and nonlinear characters. Their contribution to distinguishing between different sleep stages are compared in this article. The classifier we used for sleep stages discrimination is the random forest algorithm. The performance of the proposed approach is tested on the patients with sleep apnea by assessing accuracy, sensitivity and precision. The model achieves an accuracy of 82% to 86% for patients with varying degrees of sleep-disordered breathing, which indicates that sleep-disordered breathing does not significantly affect the performance of the proposed model. The proposed automatic scoring toolbox would alleviate the burden of the physicians, speed up sleep scoring, and expedite sleep research.
YSO-asiasanat: uni (lepotila); uniapnea-oireyhtymä; signaalianalyysi; signaalinkäsittely; MATLAB
Vapaat asiasanat: polysomnography; multi-modality analysis; MATLAB toolbox; automatic sleep scoring; sleep-disordered breathing
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2020
JUFO-taso: 1
