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

OBJECTIVE: 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. APPROACH: 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. MAIN RESULTS: 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). SIGNIFICANCE: 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.

Quantification of muscle activity during sleep for patients with neurodegenerative diseases.

Idiopathic REM sleep behavior disorder (iRBD) is a very strong predictor for later development of Parkinson's disease (PD), and is characterized by REM sleep without atonia (RSWA), resulting in increased muscle activity during REM sleep. Abundant studies have shown the loss of atonia during REM sleep, but our aim was to investigate whether iRBD and PD patients have increased muscle activity in both REM and NREM sleep compared to healthy controls. This was achieved by developing a semi-automatic algorithm for quantification of mean muscle activity per second during all sleep stages for the enrolled patients. The three groups examined included patients suffering from iRBD, PD and healthy control subjects (CO). To determine muscle activity, a baseline and threshold were established after pre-processing of the raw surface electromyography (sEMG) signal. The signal was then segmented according to the different sleep stages and muscle activity beyond the threshold was counted. The results were evaluated statistically using the two-sided Mann-Whitney U-test. The results suggested that iRBD patients also exhibit distinctive muscle activity characteristics in NREM sleep, however not as evident as in REM sleep, leading to the conclusion that RSWA still is the most distinct characteristic of RBD. Furthermore, the muscle activity of PD patients was comparable to that of controls with only slightly elevated amplitudes.