Automated sleep staging using sequential XGBoost and multi-scale temporal fusion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00179906%3A_____%2F25%3A10510538" target="_blank" >RIV/00179906:_____/25:10510538 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11150/25:10510538
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uQL.uig~oa" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uQL.uig~oa</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s44163-025-00356-z" target="_blank" >10.1007/s44163-025-00356-z</a>
Alternative languages
Result language
angličtina
Original language name
Automated sleep staging using sequential XGBoost and multi-scale temporal fusion
Original language description
Sleep stage classification is crucial in sleep medicine, but manual scoring is time-consuming, and automated solutions often struggle with complex sleep patterns. This study introduces a novel approach combining multi-scale temporal fusion with sequential XGBoost (eXtreme Gradient Boosting) processing. The approach analyzes polysomnographic data at multiple time scales (30, 15, and 5 s) while incorporating temporal context through sequential processing. Developed from direct clinical experience in sleep scoring, where experts evaluate both brief events and broader stage transitions, the method prioritizes practical applicability in sleep medicine settings. The method was validated on a clinical dataset (224 polysomnographic recordings) and the Sleep-EDF expanded database, achieving accuracy rates of 81.5% (Cohen's kappa [κ] = 0.742) on clinical data and up to 91.5% (κ = 0.826) on Sleep-EDF data. The sequential processing notably enhanced non-rapid eye movement stage 1 (N1) detection, with F1-score improvements ranging from 27 to 61% across datasets. For datasets of approximately 170 recordings, model training and validation requires up to 12 min on standard hardware. The results suggest this combined approach shows promise as a practical tool for automated sleep staging, though further research is needed to improve its performance and validate its utility across diverse clinical settings.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Discover Artificial Intelligence
ISSN
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e-ISSN
2731-0809
Volume of the periodical
5
Issue of the periodical within the volume
1
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
98
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105007627600