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

    Research output: Contribution to journalArticleAcademicpeer-review

    7 Citations (Scopus)
    2 Downloads (Pure)

    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 Sept 2018
    DOIs
    Publication statusPublished - 11 Oct 2018

    Keywords

    • Paediatric obstructive sleep apnoea-hypopnoea syndrome
    • Nocturnal oximetry
    • Portable
    • Signal processing
    • Symbolic dynamics
    • Pattern recognition
    • n/a OA procedure

    Fingerprint

    Dive into the research topics of 'Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea'. Together they form a unique fingerprint.

    Cite this