SummaryNocturnal arousals are the essential cause of disturbed sleep structure in patients with obstructive sleep apnoea syndrome (OSAS). The aim of this study was to analyse the relationship between sleep stages, respiratory (type-R) and movement (type-M) related EEG arousals. Furthermore, the value of these arousals as a criterion for the efficiency of nCPAP treatment was estimated. We examined 38 male patients aged between 30 and 71 (49.1±20.9 SD) y. All patients suffered from OSAS. The mean respiratory disturbance index (RDI) was 47.3±27.8 per h. Polysomnographic monitoring was carried out on 4 subsequent nights: baseline night, 2 nights of nCPAP titration and nCPAP control night. Sleep was visually scored and EEG arousals were classified into type R and M, depending on whether changes of respiration or movement caused the arousal. The RDI, the R index (type-R/h), the M index (type-M/h) and the R and M indices in different sleep stages were calculated. During the baseline night a deficit of slow wave sleep (SWS) and REM sleep was found. Furthermore there were more type-R than type-M arousals registered (17.4 h −1 [3.6-43.6] vs. 5.9 h −1 [1.6-11.8]) (P<0.01). They occurred during stages NREM 1, NREM 2 and REM (P<0.01). An SWS sleep rebound and a reduction of the SWS and REM latencies were already found during the first CPAP night. The R index was reduced during the first CPAP night in all sleep stages (P<0.01) and remained approximately the same in the following 2 nights (3. CPAP night: 1.1 h −1 [0.3-5.0]). Type M arousals occurred more in stages 1 and 2 (P<0.01), and remained unchanged under nCPAP. We concluded that differentiation of nocturnal arousals may provide more detailed information regarding the influence of breathing disturbances on sleep. Respiratory related, not movement related, arousals may be a useful additional tool in judging the efficiency of OSAS.
Background: Many ambulatory sleep apnea monitoring devices are equipped with software which allows an automated analysis of data as well as a visual analysis. Objective: The Merlin system which records heart rate, snoring sound, efforts, oronasal flow, body position and oxygen saturation was investigated to identify proper parameter settings for the automated analysis and to compare the automated with the visual analysis in patients with mild obstructive sleep apnea syndrome (OSAS). Sensitivity and specificity of the visual and automated analysis of ambulatory monitoring in comparison with visual polysomnographic (PSG) analysis were determined. Methods and Results: First, we tried to find the optimal parameters for the automated analysis, using 7 different settings in 17 OSAS patients. Furthermore, we applied the optimized setting to 66 OSAS patients who were admitted (age 50.9 ± 9.9 years, BMI 32.9 ± 5 kg/m2), and compared the results with the visual analysis of raw data. The patients slept for one night in the sleep laboratory with Merlin and PSG simultaneously to compare the visual and automated analysis of Merlin data with results from the visual analysis of PSG. Automated analysis leads to an underestimation of the respiratory disturbance index (RDI; p < 0.001) compared with both the visual analysis and results of PSG. Using a cutoff level of 5 apneas and hypopneas/h for the diagnosis of OSAS, the sensitivity of Merlin with the automated analysis is 40.6% and the specificity is 100%. With a cutoff level of 15/h, sensitivity and specificity rose to 91.3 and 100%, respectively, which is comparable to the visual analysis. Conclusion: Merlin is a reliable device for detection of sleep-related breathing disorders, but recordings should be analyzed visually, especially in patients with a low RDI.
Background: Heart rhythm disturbances are cardiac side effects in patients with sleep-disordered breathing (SDB), which in itself is considered to be a risk factor for bradycardic rhythm disturbances. Objective: We analyzed the prevalence and degree of SDB in patients who received a cardiac pacemaker due to bradycardic rhythm disturbances and investigated the relationship between the severity of an underlying SDB and the type of heart rhythm disturbance. Methods and Results: 192 patients (100 males, 92 females, mean age 62.2 ± 12.2 years) were studied using the portable screening device MESAM IV. The respiratory disturbance index (RDI) was calculated visually. The mean RDI in all patients was 9.13 ± 11.09/h, 11.7 ± 13.15/h in males and 6.33 ± 7.42/h in females. The prevalence ratio of SDB between men and women was 1.7:1, with significant differences in the respective severity (p < 0.05). The screening showed a prevalence of SDB (RDI > 10/h) of 32.3%. The highest prevalence was found in the group of patients with atrial fibrillation and bradycardia. However, there were no significant differences compared to other types of rhythm disturbances. The RDI in the population studied depended on age and body mass index, but not on the existence or type of rhythm disturbance and not on concomitant diseases. Conclusion: The prevalence of SDB in cardiac pacemaker patients is similar to that in patients of comparable age without a pacemaker. A heart rhythm disturbance does not seem to be an independent risk factor for development of SDB. Nevertheless, the differential diagnosis of bradycardic rhythm disturbances in this age group should include a screening for sleep apnea.
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