2026-04-14T18:51:04Zhttps://uvadoc.uva.es/oai/request

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

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 2025-01-20T17:15:10Z 2025-01-20T17:15:10Z 2018 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 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. eng info:eu-repo/semantics/restrictedAccess IOP Publishing Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea info:eu-repo/semantics/article