RT info:eu-repo/semantics/article
T1 Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis
A1 Barroso-García, Verónica
A1 Gutiérrez-Tobal, Gonzalo C.
A1 Kheirandish-Gozal, Leila
A1 Álvarez, Daniel
A1 Vaquerizo-Villar, Fernando
A1 Núñez, Pablo
A1 del Campo, Félix
A1 Gozal, David
A1 Hornero, Roberto
K1 Airflow (AF)
K1 Children
K1 Recurrence plots (RP)
K1 Sleep Apnoea-Hypopnoea Syndrome (SAHS)
K1 1203.04 Inteligencia Artificial
K1 3325.82 Procesado de señal
K1 3314 Tecnología Médica
AB Background and objective: In-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). Methods: 946 AF and blood oxygen saturation (SpO2) recordings from children ages 0–13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea–hypopnoea index (AHI). Results: 8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. Conclusions: RP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.
PB Elsevier
SN 0169-2607
YR 2020
FD 2020-01
LK https://uvadoc.uva.es/handle/10324/81493
UL https://uvadoc.uva.es/handle/10324/81493
LA eng
NO Computer Methods and Programs in Biomedicine, Enero 2020, vol. 183, p. 105083
NO Producción Científica
DS UVaDOC
RD 12-feb-2026