169934 20260306154908.0 doi 10.1016/j.compbiomed.2026.111560 sideral 148434 ART-2026-148434 eng Esparza-Iaizzo, Martín Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea 2026 Digital 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. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/EC/H2020/ 101099555/EU/BAYesian Inference with FLEXible electronics for biomedical Applications/BAYFLEX This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101099555-BAYFLEX info:eu-repo/grantAgreement/EC/H2020/ 101135782/EU/Trustworthy Efficient AI for Cloud-Edge Computing/MANOLO This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101135782-MANOLO info:eu-repo/grantAgreement/EC/H2020/ 964677/EU/Mixed Ionic and electronic Transport In Conjugated polymers for bioelectronicS/MITICS This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 964677-MITICS info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sierra-Torralba, María Klinzing, Jens G Minguez, Javier Universidad de Zaragoza (orcid)0000-0002-2957-0133 Montesano, Luis Universidad de Zaragoza (orcid)0000-0003-1183-349X López-Larraz, Eduardo (orcid)0000-0001-5482-1347 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 205 (2026), 111560 [14 pp.] Comput. biol. med. Computers in biology and medicine 0010-4825 2310566 http://zaguan.unizar.es/record/169934/files/texto_completo.pdf Versión publicada 2135262 http://zaguan.unizar.es/record/169934/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:169934 articulos driver 2026-03-06-14:50:50 ARTICLE