Quantifying Airflow Limitation and Snoring During Sleep

Tidal Volume Level Estimation Using Respiratory Sounds

Respiratory sounds have been used as a noninvasive and convenient method to estimate respiratory flow and tidal volume. However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels during sleep qualitatively. Respiratory sounds are filtered and segmented into one-minute clips, all clips are clustered into three categories: normal breathing/snoring/uncertain with agglomerative hierarchical clustering (AHC). Formant parameters are extracted to classify snoring clips into simple snoring and obstructive snoring with the K-means algorithm. For simple snoring clips, the tidal volume level is calculated based on snoring last time. For obstructive snoring clips, the tidal volume level is calculated by the maximum breathing pause interval. The performance of the proposed method is evaluated on an open dataset, PSG-Audio, in which full-night polysomnography (PSG) and tracheal sound were recorded simultaneously. The calculated tidal volume levels are compared with the corresponding lowest nocturnal oxygen saturation (LoO₂) data. Experiments show that the proposed method calculates tidal volume levels with high accuracy and robustness.

Improving Machine Learning Technology in the Field of Sleep

The authors discuss the challenges of machine- and deep learning-based automatic analysis of obstructive sleep apnea with respect to known issues with the signal interpretation, patient physiology, and the apnea-hypopnea index. Their goal is to provide guidance for sleep and machine learning professionals working in this area of sleep medicine. They suggest that machine learning approaches may well be better targeted at examining and attempting to improve the diagnostic criteria, in order to build a more nuanced understanding of the detailed circumstances surrounding OSA, rather than merely attempting to reproduce human scoring.

Palatal Sensory Function Worsens in Untreated Snorers but not in CPAP-Treated Patients With Sleep Apnea, Indicating Vibration-Induced Nervous Lesions

BACKGROUND

Signs of both motor and sensory nervous lesions have previously been shown in the upper airway of patients with OSA and habitual snorers. Snoring per se may damage all upper airway neurons over time, thereby causing progression to manifest sleep apnea. To test this hypothesis, nonsnoring subjects, untreated snorers, and CPAP-treated patients underwent repeated sensory testing of the soft palate in a prospective long-term study.

METHODS

Cold detection threshold (CDT) testing at the soft palate and lip with a thermode and nocturnal respiratory recordings were performed in 2008 to 2009 with retesting 6 to 7 years later.

RESULTS

In 25 untreated snorers, palatal CDT worsened from a median (25th-75th percentile range) 4.2°C (3.2-5.9) to 11.0°C (7.0-17.4) (P < .001). The apnea-hypopnea index increased from a median 7.0 to 14.0 events/h (P < .05). There was no significant correlation between changes in CDT and the apnea-hypopnea index. In 21 nonsnoring control subjects, palatal CDT increased from a median 3.2°C to 5.6°C (P < .005). In 19 CPAP-treated patients, palatal CDT did not significantly change; eight patients had improved values. CDTs worsened significantly more in the snorers group than in the control subjects (P < .05) and the CPAP-treated patients (P < .001). There was no significant difference between control subjects and CPAP-treated patients.

CONCLUSIONS

CDT worsened considerably over time in untreated snorers, significantly more than in nonsnoring control subjects and CPAP-treated patients. Untreated snorers therefore risk developing poor sensitivity in the upper airway. In contrast, efficient treatment of OSA seems to protect the sensory innervation, as the CPAP-treated group maintained their sensitivity to cold and, in some cases, the sensitivity even improved.

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co-morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information-rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co-morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

Digital Health and Sleep-Disordered Breathing: A Systematic Review and Meta-Analysis

STUDY OBJECTIVES

Sleep disorders in most individuals remain undiagnosed and without treatment. The use of novel tools and mobile technology has the potential to increase access to diagnosis. The objective of this study was to perform a quantitative and qualitative analysis of the available literature evaluating the accuracy of smartphones and portable devices to screen for sleep-disordered breathing (SDB).

METHODS

A literature review was performed between February 18, 2017 and March 15, 2017. We included studies evaluating adults with SDB symptoms through the use mobile phones and/or portable devices, using standard polysomnography as a comparison. A qualitative evaluation of studies was performed with the QUADAS-2 rating. A bivariate random-effects meta-analysis was used to obtain the estimated sensitivity and specificity of screening SDB for four groups of devices: bed/mattress-based, contactless, contact with three or more sensors, and contact with fewer than three sensors. For each group, we also reported positive predictive values and negative predictive values for mild, moderate, and severe obstructive sleep apnea (OSA) screening.

RESULTS

Of the 22 included studies, 18 were pooled in the meta-analysis. Devices that were bed/mattress-based were found to have the best sensitivity overall (0.921, 95% confidence interval [CI] 0.870, 0.953). The sensitivity of contactless devices to detect mild OSA cases was the highest of all groups (0.976, 95% CI 0.899, 0.995), but provided a high false positive rate (0.487, 95% CI 0.137, 0.851). The remaining groups of devices showed low sensitivity and heterogeneous results.

CONCLUSIONS

This study evidenced the limitations and potential use of portable devices in screening patients for SDB. Additional research should evaluate the accuracy of devices when used at home.

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.

OBJECTIVE

In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA.

SIGNIFICANCE

Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

Challenges and perspectives in obstructive sleep apnoea

Obstructive sleep apnoea (OSA) is a major challenge for physicians and healthcare systems throughout the world. The high prevalence and the impact on daily life of OSA oblige clinicians to offer effective and acceptable treatment options. However, recent evidence has raised questions about the benefits of positive airway pressure therapy in ameliorating comorbidities.

An international expert group considered the current state of knowledge based on the most relevant publications in the previous 5 years, discussed the current challenges in the field, and proposed topics for future research on epidemiology, phenotyping, underlying mechanisms, prognostic implications and optimal treatment of patients with OSA.

The group concluded that a revision to the diagnostic criteria for OSA is required to include factors that reflect different clinical and pathophysiological phenotypes and relevant comorbidities (e.g.nondipping nocturnal blood pressure). Furthermore, current severity thresholds require revision to reflect factors such as the disparity in the apnoea–hypopnoea index (AHI) between polysomnography and sleep studies that do not include sleep stage measurements, in addition to the poor correlation between AHI and daytime symptoms such as sleepiness. Management decisions should be linked to the underlying phenotype and consider outcomes beyond AHI.