2026-04-27T12:22:18Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/741322025-02-20T11:00:43Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea Álvarez González, Daniel Crespo, Andrea Vaquerizo Villar, Fernando Gutierrez Tobal, Gonzalo César Cerezo Hernández, Ana Barroso García, Verónica Ansermino, J. Mark Dumont, Guy A Hornero Sánchez, Roberto Campo Matias, Félix del Garde, Ainara Producción Científica Objective: This study is aimed at assessing symbolic dynamics as a reliable technique to characterise complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). Approach: Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ⩾ 5 events h−1 from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. Main results: The histogram of 3-symbol words from symbolic dynamics showed significant differences (p < 0.01) between children with AHI < 5 events h−1 and moderate-to-severe patients (AHI ⩾ 5 events h−1). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p <0.01) outperforming the other models. Significance: Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry. 2025-01-20T17:15:10Z 2025-01-20T17:15:10Z 2018 info:eu-repo/semantics/article Physiological Measurement, 2018, vol. 39, p. 104002 (16pp) 0967-3334 https://uvadoc.uva.es/handle/10324/74132 10.1088/1361-6579/aae2a8 104002 10 Physiological Measurement 39 1361-6579 eng https://iopscience.iop.org/article/10.1088/1361-6579/aae2a8/meta info:eu-repo/semantics/restrictedAccess IOP Publishing IOP Publishing SI