A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow

Abstract

The most appropriate physiological signals to develop simplified as well as accurate screening tests for obstructive sleep apnoea (OSA) remain unknown. This study aimed at assessing whether joint analysis of at-home oximetry and airflow recordings by means of machine-learning algorithms leads to a significant diagnostic performance increase compared to single-channel approaches. Consecutive patients showing moderate-to-high clinical suspicion of OSA were involved. The apnoea-hypopnoea index (AHI) from unsupervised polysomnography was the gold standard. Oximetry and airflow from at-home polysomnography were parameterised by means of 38 time, frequency, and non-linear variables. Complementarity between both signals was exhaustively inspected via automated feature selection. Regression support vector machines were used to estimate the AHI from single-channel and dual-channel approaches. A total of 239 patients successfully completed at-home polysomnography. The optimum joint model reached 0.93 (95%CI 0.90-0.95) intra-class correlation coefficient between estimated and actual AHI. Overall performance of the dual-channel approach (kappa: 0.71; 4-class accuracy: 81.3%) significantly outperformed individual oximetry (kappa: 0.61; 4-class accuracy: 75.0%) and airflow (kappa: 0.42; 4-class accuracy: 61.5%). According to our findings, oximetry alone was able to reach notably high accuracy, particularly to confirm severe cases of the disease. Nevertheless, oximetry and airflow showed high complementarity leading to a remarkable performance increase compared to single-channel approaches. Consequently, their joint analysis via machine learning enables accurate abbreviated screening of OSA at home.

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

“…These findings are in accordance with previous studies combining AF-derived features with ODI 3%, which reported not only their complementarity, but also an increase in the diagnostic performance when used together [ 30 , 32 ]. Accordingly, the complementarity of the information from AF and SpO 2 signals in the context of adult OSA [ 34 ] is also confirmed in this study using a pediatric population.…”

“…Finally, ODI 3% was computed from the SpO 2 signal as the number of desaturations greater than or equal to 3% from the baseline per hour of recording. This oximetric index has been found useful in previous approaches focused on the detection of childhood OSA [ 25 , 26 , 27 , 34 ].…”

“…However, there is a scarcity of studies regarding the joint analysis of AF and SpO 2 signals in children [ 33 ]. Only one recent study in adults focused on a direct comparison of the diagnostic ability of AF and SpO 2 signals, which evidenced their complementarity [ 34 ]. Pediatric OSA differs from the adult disease in the symptom spectrum, negative outcomes and the PSG findings, such that adult criteria cannot be used for diagnosis in children [ 1 , 3 , 5 , 9 , 35 ].…”

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children

“…A bootstrapping procedure was conducted (1000 replicates) to obtain a stable and optimal subset [ 46 ]. The average significance was used as selection threshold T s [ 30 ]. It was computed as the average number of times that all features were selected [ 30 ].…”

“…This stage was conducted following the artifact removal methods proposed in previous studies [ 12 , 17 ]. Signals whose duration was less than 3 h after artifact removal were excluded from our study [ 17 , 30 ]. Moreover, AF signals were normalized to minimize the inter-individual differences related to particular physiological characteristics other than OSA [ 31 ].…”