Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology

Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P _es) monitoring is the gold standard for measuring respiratory effort, but it is typically poorly tolerated because of its invasive nature. The objective was to investigate whether machine learning can be applied to routinely collected non-invasive, polysomnography (PSG) measures to accurately model peak negative P _es.

One thousand one hundred and nineteen patients from the Stanford Sleep Clinic with PSGs containing P _es served as the sample. The selected non-invasive PSG signals included nasal pressure, oral airflow, thoracoabdominal effort, and snoring. A long short-term memory neural network was implemented to achieve a context-based mapping between the non-invasive features and the P _es values. A hold-out dataset served as a prospective validation of the algorithm without needing to undertake a costly new study with the impractically invasive P _es.

The median difference between the measured and predicted P _es was 0.61 cmH₂O with an interquartile range (IQR) of 2.99 cmH₂O and 5th and 95th percentiles of  -5.85 cmH₂O and 5.47 cmH₂O, respectively. The model performed well when compared to actual esophageal pressure signal (ρ _median  =  0.581, p   =  0.01; IQR  =  0.298; ρ _5%  =  0.106; ρ _95%  =  0.843).

A significant difference in predicted P _es was shown between normal breathing and all obstructive SDB events; whereas, central apneas did not significantly differ from normal breathing. The developed system may be used as a tool for quantifying respiratory effort from the existing clinical practice of PSG without the need for P _es, improving characterization of SDB events as obstructive or not.