Sleep apnea is one of the most common sleep disorders. Here, patients suffer from multiple breathing pauses longer than 10 s during the night which are referred to as apneas. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive and the extensive recording equipment can have a significant impact on sleep quality falsifying the results. To overcome these problems, a comfortable and novel system for sleep monitoring based on the recording of tracheal sounds and movement data is developed. For apnea detection, a unique signal processing method utilizing both signals is introduced. Additionally, an algorithm for extracting the heart rate from body sounds is developed. For validation, ten subjects underwent a full-night PSG testing, using the developed sleep monitor in concurrence. Considering polysomnography as gold standard the developed instrumentation reached a sensitivity of 92.8% and a specificity of 99.7% for apnea detection. Heart rate measured with the proposed method was strongly correlated with heart rate derived from conventional ECG (r = 0.8164). No significant signal losses are reported during the study. In conclusion, we demonstrate a novel approach to reliably and noninvasively detect both apneas and heart rate during sleep.

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Background: In analytical performance studies, the choice of comparator method plays an important role, as studies have shown that there exist relevant systematic differences (bias) between laboratory analyzers. The feasibility of retrospective recalibration of measurement results through comparison with methods or materials of higher metrological order to minimize bias was therefore assessed. Method: Existing data from performance studies of continuous and blood glucose monitoring systems were retrospectively analyzed. Comparison with a higher-order method was performed for two different data sets. In both cases, subject samples were measured, and a subset was also measured on a higher-order method. Recalibration based on higher-order materials (standard reference material [SRM]) was conducted for two different data sets containing results from SRM and subject samples. Linear regression analysis was performed for each device separately. Resulting equations were applied to the respective complete data set of subject samples. Bias between devices in a data set across all subject samples was assessed before and after recalibration. Results: Bias between devices was reduced from −3.6% to +0.6% in one data set and from +11.0% to +0.3% in the other by recalibration based on higher-order method. Using higher-order materials, bias was also reduced by recalibration, but mixed results were found: Bias was reduced from −3.1% to −0.1% in one data set and from −4.3% to −2.7% in the other. Conclusions: Recalibration did lead to a decrease in bias and thus can reduce the impact of the choice of comparator method. The procedure should be verified in a prospectively designed setting.

Background: The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for “integrated” CGM systems. Methods: The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA. Results: The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available ( https://github.com/IfDTUlm/CGM_Performance_Assessment ). Conclusions: We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.