Symbolic dynamics to enhance diagnostic ability of portable oximetry from the phone oximeter in the detection of paediatric sleep apnoea.

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 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 and moderate-to-severe patients (AHI ≥5 events/h). 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-value <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.

Utility of bispectrum in the screening of pediatric sleep apnea-hypopnea syndrome using oximetry recordings.

BACKGROUND AND OBJECTIVE: The aim of this study was to assess the utility of bispectrum-based oximetry approaches as a complementary tool to traditional techniques in the screening of pediatric sleep apnea-hypopnea syndrome (SAHS). METHODS: 298 blood oxygen saturation (SpO2) signals from children ranging 0-13 years of age were recorded during overnight polysomnography (PSG). These recordings were divided into three severity groups according to the PSG-derived apnea hypopnea index (AHI): AHI < 5 events per hour (e/h), 5 ≤ AHI < 10 e/h, AHI ≥ 10 e/h. For each pediatric subject, anthropometric variables, 3% oxygen desaturation index (ODI3) and spectral features from power spectral density (PSD) and bispectrum were obtained. Then, the fast correlation-based filter (FCBF) was applied to select a subset of relevant features that may be complementary, excluding those that are redundant. The selected features fed a multiclass multi-layer perceptron (MLP) neural network to build a model to estimate the SAHS severity degrees. RESULTS: An optimum subset with features from all the proposed methodological approaches was obtained: variables from bispectrum, as well as PSD, ODI3, Age, and Sex. In the 3-class classification task, the MLP model trained with these features achieved an accuracy of 76.0% and a Cohen's kappa of 0.56 in an independent test set. Additionally, high accuracies were reached using the AHI cutoffs for diagnosis of moderate (AHI = 5 e/h) and severe (AHI = 10 e/h) SAHS: 81.3% and 85.3%, respectively. These results outperformed the diagnostic ability of a MLP model built without using bispectral features. CONCLUSIONS: Our results suggest that bispectrum provides additional information to anthropometric variables, ODI3 and PSD regarding characterization of changes in the SpO2 signal caused by respiratory events. Thus, oximetry bispectrum can be a useful tool to provide complementary information for screening of moderate-to-severe pediatric SAHS.