Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home

作者：Crespo Andrea^*; Alvarez Daniel; Gutierrez Tobal Gonzalo C; Vaquerizo Villar Fernando; Barroso Garcia Veronica; Alonso Alvarez Maria L; Teran Santos Joaquin; Hornero Roberto; del Campo Felix

来源：Entropy, 2017, 19(6): 284.

DOI：10.3390/e19060284

• paediatric obstructive sleep apnoea syndrome
• unattended oximetry
• multiscale entropy
• automated pattern recognition

Untreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO(2)) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO(2) recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO(2) was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.

• 出版日期2017-6