Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea. (11th October 2018)

• Record Type: Journal Article
• Authors: Álvarez, Daniel; Crespo, Andrea; Vaquerizo-Villar, Fernando; Gutiérrez-Tobal, Gonzalo C; Cerezo-Hernández, Ana; Barroso-García, Verónica; Ansermino, , J Mark; Dumont, Guy A; Hornero, Roberto; del Campo, Félix; Garde, Ainara
• Is Part Of: Physiological measurement. Volume 39:Number 10(2018:Oct.)
• Publication Date: 2018-10-11
• Subjects: paediatric obstructive sleep apnoea-hypopnoea syndrome -- nocturnal oximetry -- portable -- signal processing -- symbolic dynamics -- pattern recognition; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/aae2a8
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: 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 −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.