000169934 001__ 169934
000169934 005__ 20260306154908.0
000169934 0247_ $$2doi$$a10.1016/j.compbiomed.2026.111560
000169934 0248_ $$2sideral$$a148434
000169934 037__ $$aART-2026-148434
000169934 041__ $$aeng
000169934 100__ $$aEsparza-Iaizzo, Martín
000169934 245__ $$aAutomatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea
000169934 260__ $$c2026
000169934 5060_ $$aAccess copy available to the general public$$fUnrestricted
000169934 5203_ $$aDigital therapeutics, enabled by advanced machine learning algorithms and medical wearable devices, offer a promising approach to streamline diagnostics and improve access to healthcare. Within this framework, automatic sleep scoring can provide accurate and efficient sleep analysis from electrophysiological signals recorded with wearable sensors, such as electroencephalography (EEG). However, the optimal configuration and temporal dynamics of automatic sleep scoring systems remain unclear, especially concerning their performance across different population samples. This study systematically investigates the impact of: (1) electrode setup, (2) temporal scope, and (3) population characteristics on the performance of automatic sleep scoring algorithms. Utilizing a convolutional neural network (CNN), we analyzed various electrode configurations and temporal dynamics using datasets comprising both healthy participants and individuals with sleep apnea. Our findings reveal that sleep scoring based on a single frontal EEG channel demonstrates reliable congruency with human expert scorers, with minimal improvement observed with additional sensors. Moreover, we demonstrate that real-time sleep scoring can be achieved with comparable accuracy to offline methods, which rely on past and future information to classify a window of interest. Remarkably, a notable reduction in decoding accuracy is observed for individuals with sleep apnea compared to healthy participants, highlighting the challenges inherent in accurately assessing sleep stages in clinical populations. Digital solutions for automatic sleep scoring hold promise for facilitating timely diagnoses and personalized treatment plans, with applications extending beyond sleep analysis to include closed-loop neurostimulation interventions. Our findings provide valuable insights into the complexities of automatic sleep scoring and offer considerations for the development of effective and efficient sleep assessment tools in both clinical and research settings.
000169934 536__ $$9info:eu-repo/grantAgreement/EC/H2020/ 101099555/EU/BAYesian Inference with FLEXible electronics for biomedical Applications/BAYFLEX$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101099555-BAYFLEX$$9info:eu-repo/grantAgreement/EC/H2020/ 101135782/EU/Trustworthy Efficient AI for Cloud-Edge Computing/MANOLO$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101135782-MANOLO$$9info:eu-repo/grantAgreement/EC/H2020/ 964677/EU/Mixed Ionic and electronic Transport In Conjugated polymers for bioelectronicS/MITICS$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 964677-MITICS
000169934 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000169934 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000169934 700__ $$aSierra-Torralba, María
000169934 700__ $$aKlinzing, Jens G
000169934 700__ $$0(orcid)0000-0002-2957-0133$$aMinguez, Javier$$uUniversidad de Zaragoza
000169934 700__ $$0(orcid)0000-0003-1183-349X$$aMontesano, Luis$$uUniversidad de Zaragoza
000169934 700__ $$0(orcid)0000-0001-5482-1347$$aLópez-Larraz, Eduardo
000169934 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000169934 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000169934 773__ $$g205 (2026), 111560 [14 pp.]$$pComput. biol. med.$$tComputers in biology and medicine$$x0010-4825
000169934 8564_ $$s2310566$$uhttps://zaguan.unizar.es/record/169934/files/texto_completo.pdf$$yVersión publicada
000169934 8564_ $$s2135262$$uhttps://zaguan.unizar.es/record/169934/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000169934 909CO $$ooai:zaguan.unizar.es:169934$$particulos$$pdriver
000169934 951__ $$a2026-03-06-14:50:50
000169934 980__ $$aARTICLE