Detrended fluctuation analysis of the oximetry signal to assist in paediatric sleep apnoea–hypopnoea syndrome diagnosis

Abstract

Objective: To evaluate whether detrended fluctuation analysis (DFA) provides information that improves the diagnostic ability of the oximetry signal in the diagnosis of paediatric sleep apnoea–hypopnoea syndrome (SAHS). Approach: A database composed of 981 blood oxygen saturation (SpO2) recordings in children was used to extract DFA-derived features in order to quantify the scaling behaviour and the fluctuations of the SpO2 signal. The 3% oxygen desaturation index (ODI3) was also computed for each subject. Fast correlation-based filter (FCBF) was then applied to select an optimum subset of relevant and non-redundant features. This subset fed a multi-layer perceptron (MLP) neural network to estimate the apnoea–hypopnoea index (AHI). Main results: ODI3 and four features from the DFA reached significant differences associated with the severity of SAHS. An optimum subset composed of the slope in the first scaling region of the DFA profile and the ODI3 was selected using FCBF applied to the training set (60% of samples). The MLP model trained with this feature subset showed good agreement with the actual AHI, reaching an intra-class correlation coefficient of 0.891 in the test set (40% of samples). Furthermore, the estimated AHI showed high diagnostic ability, reaching an accuracy of 82.7%, 81.9%, and 91.1% using three common AHI cut-offs of 1, 5, and 10 events per hour (e h−1), respectively. These results outperformed the overall performance of ODI3. Significance: DFA may serve as a reliable tool to improve the diagnostic performance of oximetry recordings in the evaluation of paediatric patients with symptoms suggestive of SAHS.

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

“…AF and SpO 2 signals were preprocessed in order to remove artifacts and signal loss intervals, as well as to normalize the amplitude values. In the case of SpO 2 signals, samples with values lower than 50% of saturation and intervals with abrupt changes of oxygen saturation greater than 4% per second were removed [ 26 , 29 ]. AF signals were filtered using a low-pass filter (cutoff frequency of 1.5 Hz) and subsequently normalized [ 31 , 32 ].…”

“…FCBF was combined with bootstrapping to reduce dependency on the training data and improve generalization [ 44 , 52 ]. We obtained 1000 bootstrap replicates from the training data and the FCBF algorithm was applied to each one [ 26 , 29 , 44 ]. Features selected at least 500 times formed the optimum subset of features [ 26 , 29 , 52 ].…”

“…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 ].…”

“…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 ]. Most of these algorithms can be hardly generalizable due to their simplicity and susceptibility to overfitting the training data [ 44 ].…”