Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea

Long-term health outcomes for patients with obstructive sleep apnea: placing the Agency for Healthcare Research and Quality report in context-a multisociety commentary

This multisociety commentary critically examines the Agency for Healthcare Research and Quality (AHRQ) final report and systematic review on long-term health outcomes in obstructive sleep apnea. The AHRQ report was commissioned by the Centers for Medicare & Medicaid Services and particularly focused on the long-term patient-centered outcomes of continuous positive airway pressure, the variability of sleep-disordered breathing metrics, and the validity of these metrics as surrogate outcomes. This commentary raises concerns regarding the AHRQ report conclusions and their potential implications for policy decisions. A major concern expressed in this commentary is that the AHRQ report inadequately acknowledges the benefits of continuous positive airway pressure for several established, long-term clinically important outcomes including excessive sleepiness, motor vehicle accidents, and blood pressure. While acknowledging the limited evidence for the long-term benefits of continuous positive airway pressure treatment, especially cardiovascular outcomes, as summarized by the AHRQ report, this commentary reviews the limitations of recent randomized controlled trials and nonrandomized controlled studies and the challenges of conducting future randomized controlled trials. A research agenda to address these challenges is proposed including study designs that may include both high quality randomized controlled trials and nonrandomized controlled studies. This commentary concludes by highlighting implications for the safety and quality of life for the millions of people living with obstructive sleep apnea if the AHRQ report alone was used by payers to limit coverage for the treatment of obstructive sleep apnea while not considering the totality of available evidence.

CITATION

Patil SP, Billings ME, Bourjeily G, et al. Long-term health outcomes for patients with obstructive sleep apnea: placing the Agency for Healthcare Research and Quality report in context-a multisociety commentary. J Clin Sleep Med. 2024;20(1):135-149.

Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals

The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.

Challenges of Applying Automated Polysomnography Scoring at Scale

Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated costs, and ultimately improve the overall diagnosis of sleep disorders. Multiple and diverse strategies have been attempted for implementation of this technology at scale in the routine workflow of sleep centers. The field, however, is complex and presents unsolved challenges in a number of areas. Recent developments in computer science and artificial intelligence are nevertheless closing the gap. Technological advances are also opening new pathways for expanding our current understanding of the domain and its analysis.

Paediatric sleep apnea event prediction using nasal air pressure and machine learning

Sleep-disordered breathing is an important health issue for children. The objective of this study was to develop a machine learning classifier model for the identification of sleep apnea events taken exclusively from nasal air pressure measurements acquired during overnight polysomnography for paediatric patients. A secondary objective of this study was to differentiate site of obstruction exclusively from hypopnea event data using the model. Computer vision classifiers were developed via transfer learning to either normal breathing while asleep, obstructive hypopnea, obstructive apnea or central apnea. A separate model was trained to identify site of obstruction as either adeno-tonsillar or tongue base. In addition, a survey of board-certified and board-eligible sleep physicians was completed to compare clinician versus model classification performance of sleep events, and indicated very good performance of our model relative to human raters. The nasal air pressure sample database available for modelling comprised 417 normal, 266 obstructive hypopnea, 122 obstructive apnea and 131 central apnea events derived from 28 paediatric patients. The four-way classifier achieved a mean prediction accuracy of 70.0% (95% confidence interval [67.1-72.9]). Clinician raters correctly identified sleep events from nasal air pressure tracings 53.8% of the time, whereas the local model was 77.5% accurate. The site of obstruction classifier achieved a mean prediction accuracy of 75.0% (95% confidence interval [68.7-81.3]). Machine learning applied to nasal air pressure tracings is feasible and may exceed the diagnostic performance of expert clinicians. Nasal air pressure tracings of obstructive hypopneas may "encode" information regarding the site of obstruction, which may only be discernable by machine learning.