Monitoring ultradian changes in cardiorespiratory control in obstructive sleep apnea syndrome

A sleep stage estimation algorithm based on cardiorespiratory signals derived from a suprasternal pressure sensor

Automatic estimation of sleep structure is an important aspect in moving sleep monitoring from clinical laboratories to people's homes. However, the transition to more portable systems should not happen at the expense of important physiological signals, such as respiration. Here, we propose the use of cardiorespiratory signals obtained by a suprasternal pressure (SSP) sensor to estimate sleep stages. The sensor is already used for diagnosis of sleep-disordered breathing (SDB) conditions, but besides respiratory effort it can detect cardiac vibrations transmitted through the trachea. We collected the SSP sensor signal in 100 adults (57 male) undergoing clinical polysomnography for suspected sleep disorders, including sleep apnea syndrome, insomnia, and movement disorders. Here, we separate respiratory effort and cardiac activity related signals, then input these into a neural network trained to estimate sleep stages. Using the original mixed signal the results show a moderate agreement with manual scoring, with a Cohen's kappa of 0.53 in Wake/N1-N2/N3/rapid eye movement sleep discrimination and 0.62 in Wake/Sleep. We demonstrate that decoupling the two signals and using the cardiac signal to estimate the instantaneous heart rate improves the process considerably, reaching an agreement of 0.63 and 0.71. Our proposed method achieves high accuracy, specificity, and sensitivity across different sleep staging tasks. We also compare the total sleep time calculated with our method against manual scoring, with an average error of -1.83 min but a relatively large confidence interval of ±55 min. Compact systems that employ the SSP sensor information-rich signal may enable new ways of clinical assessments, such as night-to-night variability in obstructive sleep apnea and other sleep disorders.

Model-Derived Markers of Autonomic Cardiovascular Dysfunction in Sleep-Disordered Breathing

Evidence indicates that sleep-disordered breathing leads to elevated sympathetic tone and impaired vagal activity, promoting hypertension and cardiometabolic disease. Low-cost but accurate monitoring of autonomic function is useful for the aggressive management of sleep apnea. This article reviews the development and application of multivariate dynamic biophysical models that enable the causal dependencies among respiration, blood pressure, heart rate variability, and peripheral vascular resistance to be quantified. The markers derived from these models can be used in conjunction with heart rate variability to increase the sensitivity with which abnormalities in autonomic cardiovascular control are detected in subjects with sleep-disordered breathing.

Autonomic markers of impaired glucose metabolism: effects of sleep-disordered breathing

BACKGROUND

The association between diabetes and abnormalities in autonomic function is well-known, but it is not clear if this association can be extended to subjects with prediabetic impaired glucose metabolism (IGM). Sleep-disordered breathing (SDB), which commonly occurs in this population, is often overlooked. We sought to determine how autonomic function, monitored in an overnight sleep study setting, may be impaired in subjects with IGM and/or SDB.

METHODS

Polysomnograms (PSGs) selected from the Cleveland Family Study database were categorized into four groups: normal, SDB (respiratory disturbance index > 5/h), IGM, and both SDB and IGM. Impaired glucose metabolism was defined as an oral glucose tolerance test (OGTT) level > 140 mg/dl. Time-domain and frequency-domain indices of heart rate variability were used to quantify autonomic impairment. Baroreflex sensitivity determined using pulse transit time (BRS(PTT)), an indirect measure of baroreflex sensitivity based on spontaneous pulse transit time fluctuations, was used as a surrogate measure of baroreflex sensitivity.

RESULTS

Based on 31 PSGs from subjects (16 males, 15 females) ages 20.8-61.2 years, both SDNN and BRS(PTT) were found to be 20-25% lower in SDB and ~40% lower in IGM and SDB + IGM as compared to subjects without either condition. In analyses of continuous measures, mean standard deviation of 5 min R-R intervals (SDNN) and BRS(PTT) were found to be negatively correlated with OGTT following adjustment for age and body mass index. Oral glucose tolerance test and age were the two most significant factors for predicting SDNN and BRS(PTT).

CONCLUSIONS

Our analyses suggest that cardiac autonomic control is impaired in IGM, regardless of whether SDB is present. The abnormal autonomic function involves degradation of baroreflex regulation.