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- sleep apnoea (2)
- sleep-disordered breathing (2)
- Bunker cave (1)
- Cardiac rehabilitation (1)
- Dansgaard-Oeschger event (1)
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- Marine isotope stage 3 (1)
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Marine Isotope Stage 3 (MIS 3, 57-27 ka) was characterised by numerous rapid climate oscillations (i.e., Dansgaard-Oeschger (D/O-) events), which are reflected in various climate archives. So far, MIS 3 speleothem records from central Europe have mainly been restricted to caves located beneath temperate Alpine glaciers or close to the Atlantic Ocean. Thus, MIS 3 seemed to be too cold and dry to enable speleothem growth north of the Alps in central Europe. Here we present a new speleothem record from Bunker Cave, Germany, which shows two distinct growth phases from 52.0 (+0.8, -0.5) to 50.9 (+0.6, -1.3) ka and 473 (+1.0, -0.6) to 42.8 (+/- 0.9) ka, rejecting this hypothesis. These two growth phases potentially correspond to the two warmest and most humid phases in central Europe during MIS 3, which is confirmed by pollen data from the nearby Eifel. The hiatus separating the two phases is associated with Heinrich stadial 5 (HS 5), although the growth stop precedes the onset of HS 5. The first growth phase is characterised by a fast growth rate, and Mg concentrations and Sr isotope data suggest high infiltration and the presence of soil cover above the cave. The second growth phase was characterised by drier, but still favourable conditions for speleothem growth. During this phase, the delta C-13 values show a significant decrease associated with D/O-event 12. The timing of this shift is in agreement with other MIS 3 speleothem data from Europe and Greenland ice core data. (C) 2018 Elsevier Ltd. All rights reserved.
Interest in developing a new method of man-to-machine communication-a brain-computer interface (BCI)-has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms
BERNDT, K.-P.: Interdisziplinäres multimediales Lehr- und Lernprojekt "Biodiversität in Großökosystemen" ; FELGENTREFF, C.: Hochwasser im Kontext. Zur Rekonstruktion des Oderhochwassers 1997 in Brandenburg und seiner Folgen ; GIEST, H.; WALGENBACH, W.: Entwicklung von Multimedia-Bausteinen zur Ökologischen Grundbildung ; GREIL, H. et al.: Anthropometrische Grundlagen für die Entwicklung maßgerechter 3D-Computersimulationen des menschlichen Körpers zum Einsatz bei der Gestaltung körpernaher Umweltelemente ; SCHEFFLER, C. et al.: Maß- und funktionsgerechte Gestaltung der körpernahen Umwelt älterer Menschen ; SCHILITZ, A. et al.: Körperliche Entwicklung von Brandenburger Schülern und Schülerinnen als Indikator für komplexe Veränderungen von Umweltbedingungen ; SEGERT, A.; ZIERKE, I.: Handlungsstrukturen in einer ökologisch orientierten Genossenschaft ; WIPPER, R.: Interaktive multimediale Bildungssoftware "Ökosystem Wald - Artenvielfalt in Brandenburg"
A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.
Aim To determine the prevalence of, and the risk factors for, sleep apnoea in cardiac rehabilitation (CR) facilities in Germany.
Methods 1152 patients presenting for CR were screened for sleep-disordered breathing with 2-channel polygraphy (ApneaLink; ResMed). Parameters recorded included the apnoea-hypopnoea index (AHI), number of desaturations per hour of recording (ODI), mean and minimum nocturnal oxygen saturation and number of snoring episodes. Patients rated subjective sleep quality on a scale from 1 (poor) to 10 (best) and completed the Epworth Sleepiness Scale (ESS).
Results Clinically significant sleep apnoea (AHI 15/h) was documented in 33% of patients. Mean AHI was 1416/h (range 0-106/h). Sleep apnoea was defined as being of moderate severity in 18% of patients (AHI 15-29/h) and severe in 15% (AHI 30/h). There were small, but statistically significant, differences in ESS score and subjective sleep quality between patients with and without sleep apnoea. Logistic regression model analysis identified the following as risk factors for sleep apnoea in CR patients: age (per 10 years) (odds ratio (OR) 1.51; p<0.001), body mass index (per 5 units) (OR 1.31; p=0.001), male gender (OR 2.19; p<0.001), type 2 diabetes mellitus (OR 1.45; p=0.040), haemoglobin level (OR 0.91; p=0.012) and witnessed apnoeas (OR 1.99; p<0.001).
Conclusions The findings of this study indicate that more than one-third of patients undergoing cardiac rehabilitation in Germany have sleep apnoea, with one-third having moderate-to-severe SDB that requires further evaluation or intervention. Inclusion of sleep apnoea screening as part of cardiac rehabilitation appears to be appropriate.
Risk factors for, and prevalence of, sleep apnoea in cardiac rehabilitation facilities in Germany
(2015)
Aim To determine the prevalence of, and the risk factors for, sleep apnoea in cardiac rehabilitation (CR) facilities in Germany.
Methods 1152 patients presenting for CR were screened for sleep-disordered breathing with 2-channel polygraphy (ApneaLink; ResMed). Parameters recorded included the apnoea-hypopnoea index (AHI), number of desaturations per hour of recording (ODI), mean and minimum nocturnal oxygen saturation and number of snoring episodes. Patients rated subjective sleep quality on a scale from 1 (poor) to 10 (best) and completed the Epworth Sleepiness Scale (ESS).
Results Clinically significant sleep apnoea (AHI 15/h) was documented in 33% of patients. Mean AHI was 1416/h (range 0-106/h). Sleep apnoea was defined as being of moderate severity in 18% of patients (AHI 15-29/h) and severe in 15% (AHI 30/h). There were small, but statistically significant, differences in ESS score and subjective sleep quality between patients with and without sleep apnoea. Logistic regression model analysis identified the following as risk factors for sleep apnoea in CR patients: age (per 10 years) (odds ratio (OR) 1.51; p<0.001), body mass index (per 5 units) (OR 1.31; p=0.001), male gender (OR 2.19; p<0.001), type 2 diabetes mellitus (OR 1.45; p=0.040), haemoglobin level (OR 0.91; p=0.012) and witnessed apnoeas (OR 1.99; p<0.001).
Conclusions The findings of this study indicate that more than one-third of patients undergoing cardiac rehabilitation in Germany have sleep apnoea, with one-third having moderate-to-severe SDB that requires further evaluation or intervention. Inclusion of sleep apnoea screening as part of cardiac rehabilitation appears to be appropriate.