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

Abstract

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.

Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost

“…SampEn is a statistic used to measure irregularity in biomedical signals [ 49 ]. It has been widely employed to characterize fluctuations of AF and SpO 2 signals [ 18 , 20 , 27 , 49 ]. Given a signal of length N , SampEn is defined as the negative logarithm of the conditional probability of two similar sequences of length m remaining similar (distance lower than r ) after the length of the sequence increases in one sample (length m + 1) [ 20 , 49 ]: where A m ( r ) and B m ( r ) are the average number of similar sequences of length m + 1 and m , respectively.…”

“…Finally, ODI 3% was computed from the SpO 2 signal as the number of desaturations greater than or equal to 3% from the baseline per hour of recording. This oximetric index has been found useful in previous approaches focused on the detection of childhood OSA [ 25 , 26 , 27 , 34 ].…”

“…Previous studies focused on the assessment of AF or SpO 2 signals as simplified alternatives to PSG [ 15 , 16 , 17 ]. They involved adult [ 18 , 19 , 20 , 21 , 22 ] and pediatric [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] subjects as well as different automated signal processing methods. Regarding automatic analysis of SpO 2 signals, several methods have been proposed to detect OSA in children.…”

“…Regarding automatic analysis of SpO 2 signals, several methods have been proposed to detect OSA in children. These studies comprised time and frequency domain analyses [ 23 , 24 , 25 , 26 ], as well as nonlinear methods and various oximetric indexes [ 25 , 26 , 27 , 28 , 29 ]. Studies involving AF signals comprised spectral analysis [ 30 ], irregularity and variability analyses [ 31 ], and nonlinear recurrence plots [ 32 ].…”

“…From a machine learning point of view, previous studies focused on the automatic detection of pediatric OSA using simple and widespread algorithms. These approaches included Logistic Regression (LR) [ 23 , 24 , 25 , 27 , 28 , 30 , 31 , 36 , 37 ], Linear or Quadratic Discriminant Analysis (LDA, QDA) [ 38 , 39 , 40 ], and Support Vector Machines (SVM) [ 41 ]. Other recent studies relied on more complex Multilayer Perceptron (MLP) neural networks [ 26 , 29 , 32 , 42 , 43 ].…”