Intensity of respiratory cortical arousals is a distinct pathophysiologic feature and is associated with disease severity in obstructive sleep apnea patients

Background: We investigated whether the number, duration and intensity of respiratory arousals (RA) on C3-electroencephalographic (EEG) recordings correlate with polysomnography (PSG)-related disease severity in obstructive sleep apnea (OSA) patients. We also investigated if every patient might have an individual RA microstructure pattern, independent from OSA-severity. Methods: PSG recordings of 20 OSA patients (9 female; age 27–80 years) were analyzed retrospectively. Correlation coefficients were calculated between RA microstructure (duration, EEG-intensity) and RA number and respiratory disturbance index (RDI), oxygen desaturation index (ODI) and arousal index (AI). Intraclass correlations (ICC) for both RA duration and intensity were calculated. Sleep stage-specific and apnea- and hypopnea-specific analyses were also done. The probability distributions of duration and intensity were plotted, interpolated with a kernel which fits the distribution. A Bayesian posterior distribution analysis and pair-wise comparisons of each patient with all other 19 patients were performed. Results: Of the analyzed 2600 RA, strong positive correlations were found between average RA intensity and both RDI and AI. The number of PSG-recorded RA was strongly positively correlated with RDI. Significant correlations between average RA intensity in REM, NREM2 and NREM3 sleep stages and total ODI were identified. No sleep stage-specific correlations of arousal microstructure with age, sex, RDI or AI were identified. Although between-subjects ICC values were 0.7 (all p < 0.05). While apnea-related RA duration did not differ from hypopnea-related RA duration, RA intensity was significantly higher (p = 0.00135) in hypopneas than in apneas. A clear individual pattern of arousal duration for each patient was made distinct. For arousal intensity, a Gaussian distribution was identified in most patients. The Bayesian statistics regarding the arousal microstructure showed significant differences between each pair of patients. Conclusions: Each individual patient with OSA might have an individual pattern of RA intensity and duration indicating a distinct individual pathophysiological feature. Arousal intensity was significantly higher in hypopneic than in apneic events and may be related causally to the diminished (compared to apneas) respiratory distress associated with hypopneas. RA intensity in REM, NREM2 and NREM3 strongly correlated with ODI

Heart rate variability as a surrogate marker of severe chronic coronary syndrome in patients with obstructive sleep apnea

Background and Objectives: Obstructive sleep apnea (OSA) is a known risk factor for chronic coronary syndrome (CCS). CCS and OSA are separately associated with significant changes in heart rate variability (HRV). In this proof-of-concept study, we tested whether HRV values are significantly different between OSA patients with concomitant severe CCS, and OSA patients without known CCS. Material and Methods: The study comprised a retrospective assessment of the historical and raw polysomnography (PSG) data of 32 patients who presented to a tertiary university hospital with clinical complaints of OSA. A total of 16 patients (four females, mean age 62.94 ± 2.74 years, mean body mass index (BMI) 31.93 ± 1.65 kg/m2) with OSA (median apnea-hypopnea index (AHI) 39.1 (30.5–70.6)/h) and severe CCS were compared to 16 patients (four females, mean age 62.35 ± 2.06 years, mean BMI 32.19 ± 1.07 kg/m2) with OSA (median AHI 40 (30.6–44.5)/h) but without severe CCS. The short–long-term HRV (in msec) was calculated based on the data of a single-lead electrocardiogram (ECG) provided by one full-night PSG, using the standard deviation of the NN, normal-to-normal intervals (SDNN) and the heart rate variability triangular index (HRVI) methods, and compared between the two groups. Results: A significant reduction (p < 0.05) in both SDNN and HRVI was found in the OSA group with CCS compared to the OSA group without CCS. Conclusions: Severe CCS has a significant impact on short–long-term HRV in OSA patients. Further studies in OSA patients with less-severe CCS may shed more light onto the involved mechanistic processes. If confirmed in future larger studies, this physiologic metric has the potential to provide a robust surrogate marker of severe CCS in OSA patients

Current treatment of comorbid insomnia and obstructive sleep apnea with CBTI and PAP-therapy : a systematic review

Insomnia and obstructive sleep apnea (OSA) are often both present in patients with sleep- disordered-breathing. The coexistence of the two disorders shows an increase in cumulative morbidity and an overall greater illness severity. There is still considerable controversy regarding management decisions in this group of patients. This systematic review focused on more recent evidence regarding treatment of patients presenting with both clinical entities of comorbid insomnia and obstructive sleep apnea in terms of their management, especially using combinations of positive airway pressure (PAP, namely aPAP, cPAP, adaptive servo-ventilation[ASV]) and CBTi as well as each one of these two modalities alone. As a conclusion it is necessary to specifically target distinct combinations of both insomnia (initial, middle, late) and OSA (mild, moderate, severe) phenotypes. The present review gives reason to assume that both CBTi and PAP-therapy are necessary. However, it appears that distinct treatment patterns may suit different COMISA phenotypes

A novel quantitative arousal-associated EEG-metric to predict severity of respiratory distress in obstructive sleep apnea patients

Respiratory arousals (RA) on polysomnography (PSG) are an important predictor of obstructive sleep apnea (OSA) disease severity. Additionally, recent reports suggest that more global indices of desaturation such as the hypoxic burden, namely the area under the curve (AUC) of the oxygen saturation (SaO2) PSG trace may better depict the desaturation burden in OSA. Here we investigated possible associations between a new metric, namely the AUC of the respiratory arousal electroencephalographic (EEG) recording, and already established parameters as the apnea/hypopnea index (AHI), arousal index and hypoxic burden in patients with OSA. In this data-driven study, polysomnographic data from 102 patients with OSAS were assessed (32 female; 70 male; mean value of age: 52 years; mean value of Body-Mass-Index-BMI: 31 kg/m2). The marked arousals from the pooled EEG signal (C3 and C4) were smoothed and the AUC was estimated. We used a support vector regressor (SVR) analysis to predict AHI, arousal index and hypoxic burden as captured by the PSG. The SVR with the arousal-AUC metric could quite reliably predict the AHI with a high correlation coefficient (0,58 in the training set, 0,65 in the testing set and 0,64 overall), as well as the hypoxic burden (0,62 in the training set, 0,58 in the testing set and 0,59 overall) and the arousal index (0,58 in the training set, 0,67 in the testing set and 0,66 overall). This novel arousal-AUC metric may predict AHI, hypoxic burden and arousal index with a quite high correlation coefficient and therefore could be used as an additional quantitative surrogate marker in the description of obstructive sleep apnea disease severity

Corticoperipheral neuromuscular disconnection in obstructive sleep apnoea

The roles of central nervous mechanisms and cortical output in obstructive sleep apnea remain unclear. We addressed corticomuscular coupling between cortical sensorimotor areas and lower facial motor units as a mechanistic pathway and as a possible surrogate marker of cortico-peripheral motor control in obstructive sleep apnea. In this exploratory cross-sectional retrospective study we analysed EEG (C3- and C4-leads) and chin EMG from polysomnography recordings in 86 participants (22 females; age range: 26-81 years), 27 with mild (respiratory disturbance index = 5-15 events/hour), 21 with moderate (15-30 events/h) and 23 with severe obstructive sleep apnea (> 30 events/h) and 15 control subjects (<5 events/h). By computing C3-/C4-EEG- chin EMG coherence of signal dynamics in time and frequency domains we investigated corticomuscular coupling between cortical sensorimotor areas and lower facial motor units with increasing obstructive sleep apnea severity during the entire sleeping time, during different sleep stages and during obstructive respiratory events, including 5 seconds before (stable breathing) and after events (breathing resumption). Additionally, we studied a possible influence of body-mass-index and autonomic nervous system activation. We found that both average and respiratory event-specific corticomuscular coupling between cortical sensorimotor areas and lower facial motor units weakened significantly with increasing obstructive sleep apnea severity, was strongest during N3 and weakened in N1, N2 and rapid-eye-movement stages (in decreasing order). Coupling increases significantly during the obstructive respiratory events compared with coupling just before and following them. Results were independent of body-mass-index or autonomic nervous system activation. We conclude that obstructive respiratory events in obstructive sleep apnea are very strongly associated both quantitatively and temporally with the degree of disconnection within the cortical sensorimotor areas - lower facial motor units pathway. This quite coordinated activity pattern suggests a cortical sensorimotor area-driven obstructive respiratory event pattern generator and a central motor output disorder in obstructive sleep apnea

An AI-supported diagnostic tool for obstructive sleep apnea patients based on delta-alpha connectivity at the sensorimotor cortex [Abstract]

Background: The modulation of delta-alpha phase-amplitude cross-frequency coupling (PACFC) may influence information processing throughout the human cerebral cortex. We investigated whether this frequency band-specific modulation is impaired in patients with obstructive sleep apnea (OSA). Patients & Methods: In this study, the C3- and C4- electroencephalographic recordings of 170 participants (86 in main dataset: age 27-84 years, 44 subjects had moderate or severe OSA with respiratory disturbance index RDI>15/h; 84 in validation dataset: age 35 -75 years, 42 subjects with RDI>15/h) who underwent full-night polysomnography (PSG) were evaluated. We tested if the delta-alpha PACFC modulation index (MI) at the sensorimotor cortex differs between OSA patients with RDI>15/h and those with RDI≤15/h in distinct sleep stages. Further, by making use of a Support Vector Machine (SVM) algorithm, we tested if the sleep stage – specific MIs could predict RDI values of OSA patients. Results: In both datasets, in OSA patients with RDI >15/h, the delta-alpha CFC-MI was significantly (p< 0.05) reduced at the sensorimotor cortex during REM and NREM1 stages, while increased during NREM2 compared to patients with RDI ≤15/h. In addition, the delta-alpha MI in REM sleep stage could provide with use of an SVM algorithm a quite reliable (82% accuracy) prediction of the RDI in OSA patients. Conclusions: This increase in disconnection at the cortical sensorimotor areas with increasing respiratory distress during sleep further supports the concept of a cortical sensorimotor dysfunction in OSA patients. Additionally, the delta – alpha MI during REM sleep may provide an objective neurophysiologic surrogate marker of respiratory distress in OSA patients

Sleep stage classification using spectral analyses and support vector machine algorithm on C3- and C4-EEG signals [Abstract]

Introduction Sleep stage classification currently relies largely on visual classification methods. We tested a new pipeline for automated offline classification based upon power spectrum at six different frequency bands. The pipeline allowed sleep stage classification and provided whole-night visualization of sleep stages. Materials and methods 102 subjects (69 male; 53.74 ± 12.4 years) underwent full-night polysomnography. The recording system included C3- and C4-EEG channels. All signals were measured at sampling rate of 200 Hz. Four epochs (30 seconds each) of each sleep stage (N1, N2, N3, REM, awake) were marked in the visually scored recordings of each one of the 102 patients. Scoring of sleep stages was performed according to AASM 2007-criteria. In total 408 epochs for each sleep stage were included in the sleep stage classification analyses. Recordings of all these epochs were fed into the pipeline to estimate the power spectrum at six different frequency bands, namely from very low frequency (VLF, 0.1-1 Hz) to gamma frequency (30-50 Hz). The power spectrum was measured with a method called multitaper method. In this method the spectrum is estimated by multiplying the data with K windows (i.e tapers).The estimated parameters were given as input to the support vector machine (SVM) algorithm to classify the five different sleep stages based on the mean power amplitude estimated from six different frequency bands. The SVM algorithm was trained with 51 subjects and the testing was done with the other 51 subjects. In order to avoid bias of the training dataset, a 10-fold cross validation was additionally done to check the performance of the SVM algorithm Results The estimated testing accuracy of prediction of the sleep stages was 84.1% for stage N1 using the mean power amplitude from the delta frequency band. Accuracy was 67.8% for stage N2 from the delta frequency band and 74.9% for stage N3 from the VLF. Accuracy was 79.7% for REM stage from the delta frequency band and 84,8% for the wake stage from the theta frequency band. Conclusions We were able to successfully classify the sleep stages using the mean power amplitude at six different frequency bands separately and achieved up to 85% accuracy using the electrophysiological EEG signals. The delta and theta frequency bands gave the best accuracy of classification among all sleep stages

EEG-EMG-coherence in SDB patients with utilization of a support vector machine-algorithm [Poster Abstract]

Background We investigated whether the EEG-EMG-coherence allows a differentiation between patients with sleep-disordered breathing (SDB) without OSA and SDB-patients with mild, moderate or severe OSA. Methods Polysomnographic recordings of 102 patients with SDB (33 female; age: 53,± 12,4 years) were analyzed with the multitaper coherence method (MTM). Recordings contained 2 EEG-channels (C3 and C4) and a chin EMG-channel for one night. Four epochs (each 30 seconds, classified manually by AASM 2007 criteria) of each sleep stage were marked (1632 epochs in total), which were included in the classification analysis. The collected data sets were supplied to the support vector machine (SVM) algorithm to classify OSA severity. Twenty patients had a mild (RDI ≥10/h and < 15/h), 30 patients had a moderate (RDI ≥15/h and < 30/h) and 27 patients had a severe OSA (RDI ≥30/h). 25 patients had a RDI < 10/h. The AUC (area under the curve) value was calculated for each receiver operator curve (ROC) curve. Results EEG-EMG coherence was able to distinguish between the SDB-patients without OSA and SDB-patients with OSA in each of the 3 severity groups using an SVM algorithm. In mild OSA, the AUC was 0.616 (p = 0.024), in moderate OSA the AUC was 0.659 (p = 0.003), and in severe OSA the AUC was 0.823 (p < 0.001). Conclusions SDB patients with OSA can be differentiated from SDB patients without OSA on the basis of EEG-EMG coherence by using the Multitaper Coherence Method (MTM) and SVM algorithm

Medical and Dental Students' Perception of Interdisciplinary Knowledge, Teaching Content, and Interprofessional Status at a German University: A Cross-Sectional Study.

Although oral health is considered a key indicator of overall health, dentistry is still neglected in medical education at the university level. Interprofessional education (IPE) is an important tool to promote collaboration among health care providers and to reduce barriers to access in health care. In this cross-sectional study, medical and dental students at Mainz University, Germany, were surveyed regarding their perception of interdisciplinary knowledge, teaching content, interprofessional standing, and attitudes toward IPE. Spearman's rank correlation was used to identify associated statements. Structural equation modeling (SEM) was performed to understand how sex, study progress, and prior education might influence student attitudes. In total, 426 medical students and 211 dental students were included in the study. Dental students rated their interdisciplinary knowledge higher than medical students. The relevance of IPE as assessed by the students correlated significantly with their motivation to continue IPE after graduation. Both groups of students valued the other discipline but rejected a combined graduate program. Students with prior professional training valued the synergy of medicine and dentistry more the students without prior training. Interprofessional knowledge and interest in IPE was higher among dental students. Understanding students' attitudes toward IPE is an important prerequisite for adapting university curricula to strengthen students' attitudes and motivation