128117 20260217205449.0 doi 10.1109/JBHI.2023.3331947 sideral 135343 ART-2024-135343 eng Cajal, Diego Universidad de Zaragoza Obstructive Sleep Apnea Screening by Joint Saturation Signal Analysis and PPG-derived Pulse Rate Oscillations 2024 Access copy available to the general public Unrestricted Obstructive sleep apnea (OSA) is a high-prevalence disease in the general population, often underdiagnosed. The gold standard in clinical practice for its diagnosis and severity assessment is the polysomnography, although in-home approaches have been proposed in recent years to overcome its limitations. Today's ubiquitously presence of wearables may become a powerful screening tool in the general population and pulse-oximetry-based techniques could be used for early OSA diagnosis. In this work, the peripheral oxygen saturation together with the pulse-to-pulse interval (PPI) series derived from photoplethysmography (PPG) are used as inputs for OSA diagnosis. Different models are trained to classify between normal and abnormal breathing segments (binary decision), and between normal, apneic and hypopneic segments (multiclass decision). The models obtained 86.27% and 73.07% accuracy for the binary and multiclass segment classification, respectively. A novel index, the cyclic variation of the heart rate index (CVHRI), derived from PPI's spectrum, is computed on the segments containing disturbed breathing, representing the frequency of the events. CVHRI showed strong Pearson's correlation (r) with the apnea-hypopnea index (AHI) both after binary (r=0.94, p < 0.001) and multiclass (r=0.91, p < 0.001) segment classification. In addition, CVHRI has been used to stratify subjects with AHI higher/lower than a threshold of 5 and 15, resulting in 77.27% and 79.55% accuracy, respectively. In conclusion, patient stratification based on the combination of oxygen saturation and PPI analysis, with the addition of CVHRI, is a suitable, wearable friendly and low-cost tool for OSA screening at home. info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PID2021-126734OB-C21 info:eu-repo/grantAgreement/ES/MICIU-FEDER/PDC2021-120775-I00 info:eu-repo/grantAgreement/ES/MICIU-FEDER/PID2019-104881RB-I00 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 6.8 2024 COMPUTER SCIENCE, INFORMATION SYSTEMS 21 / 258 = 0.081 2024 Q1 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 3 / 67 = 0.045 2024 Q1 T1 MEDICAL INFORMATICS 6 / 48 = 0.125 2024 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 23 / 177 = 0.13 2024 Q1 T1 1.649 2024 Biotechnology 2024 Q1 Computer Science Applications 2024 Q1 Health Information Management 2024 Q1 Health Informatics 2024 Q1 Electrical and Electronic Engineering 2024 Q1 13.5 2024 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Gil, Eduardo Universidad de Zaragoza (orcid)0000-0001-7285-0715 Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-3434-9254 Varon, Carolina Testelmans, Dries Buyse, Bertien Jensen, Chris Hoare, Rohan Bailón, Raquel Lázaro, Jesús Universidad de Zaragoza (orcid)0000-0001-8742-0072 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 28, 1 (2024), 37948138 [11 pp.] IEEE j. biomed. health inform. IEEE journal of biomedical and health informatics 2168-2194 1105377 http://zaguan.unizar.es/record/128117/files/texto_completo.pdf Postprint 3578744 http://zaguan.unizar.es/record/128117/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:128117 articulos driver 2026-02-17-20:18:30 ARTICLE