Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea

Daniel Alvarez (Corresponding Author), Andrea Crespo, Fernando Vaquerizo-Villar, Gonzalo C. Gutierrez-Tobal, Ana Cerezo-Hernandez, Veronica Barroso-Garcia, J. Mark Ansermino, Guy A. Dumont, Roberto Hornero, Felix del Campo, Ainara Garde

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    Abstract

    Objective: This study is aimed at assessing symbolic dynamics as a reliable technique to characterize 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−1 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−1 and moderate-to-severe patients (AHI ⩾ 5 events h−1). 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 <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.
    Original languageEnglish
    Article number104002
    Number of pages16
    JournalPhysiological measurement
    Volume39
    Issue number10
    Early online date19 Sep 2018
    DOIs
    Publication statusPublished - 11 Oct 2018

    Keywords

    • paediatric obstructive sleep apnoea-hypopnoea syndrome
    • nocturnal oximetry
    • portable
    • signal processing
    • symbolic dynamics
    • pattern recognition

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