Thomas Penzel

Cohort Profile: The Study of Health in Pomerania

Henry Volzke, y Dietrich Alte,1y Carsten Oliver Schmidt, Dorte Radke, Roberto Lorbeer, Nele Friedrich, Nicole Aumann, Katharina Lau, Michael Piontek, Gabriele Born, Christoph Havemann, Till Ittermann, Sabine Schipf, Robin Haring, Sebastian E Baumeister, Henri Wallaschofski, Matthias Nauck, Stephanie Frick, Andreas Arnold, Michael Junger, Julia Mayerle, Matthias Kraft, Markus M Lerch, Marcus Dorr, Thorsten Reffelmann, Klaus Empen, Stephan B Felix, Anne Obst, Beate Koch, Sven Glaser, Ralf Ewert, Ingo Fietze, Thomas Penzel, Martina Doren, Wolfgang Rathmann, Johannes Haerting, Mario Hannemann, Jurgen Ropcke, Ulf Schminke, Clemens Jurgens, Frank Tost, Rainer Rettig, Jan A Kors, Saskia Ungerer, Katrin Hegenscheid, Jens-Peter Kuhn, Julia Kuhn, Norbert Hosten, Ralf Puls, Jorg Henke, Oliver Gloger, Alexander Teumer, Georg Homuth, Uwe Volker, Christian Schwahn, Birte Holtfreter, Ines Polzer, Thomas Kohlmann, Hans J Grabe, Dieter Rosskopf, Heyo K Kroemer, Thomas Kocher, Reiner Biffar,17,y Ulrich John20y and Wolfgang Hoffmann1y

Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Sleep apnoea is a common disorder that is usually diagnosed through expensive studies conducted in sleep laboratories. Sleep apnoea is accompanied by a characteristic cyclic variation in heart rate or other changes in the waveform of the electrocardiogram (ECG). If sleep apnoea could be diagnosed using only the ECG, it could be possible to diagnose sleep apnoea automatically and inexpensively from ECG recordings acquired in the patients home. This study had two parts. The first was to assess the ability of an overnight ECG recording to distinguish between patients with and without apnoea. The second was to assess whether the ECG could detect apnoea during each minute of the recording. An expert, who used additional physiological signals, assessed each of the recordings for apnoea. Research groups were invited to access data via the world-wide web and submit algorithm results to an international challenge linked to a conference. A training set of 35 recordings was made available for algorithm development, and results from a test set of 35 different recordings were made available for independent scoring. Thirteen algorithms were compared. The best algorithms made use of frequency-domain features to estimate changes in heart rate and the effect of respiration on the ECG waveform. Four of these algorithms achieved perfect scores of 100% in the first part of the study, and two achieved an accuracy of over 90% in the second part of the study.

Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea

Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.

Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments

We study correlation properties of the magnitude and the sign of the increments in the time intervals between successive heartbeats during light sleep, deep sleep, and rapid eye movement (REM) sleep using the detrended fluctuation analysis method. We find short-range anticorrelations in the sign time series, which are strong during deep sleep, weaker during light sleep, and even weaker during REM sleep. In contrast, we find long-range positive correlations in the magnitude time series, which are strong during REM sleep and weaker during light sleep. We observe uncorrelated behavior for the magnitude during deep sleep. Since the magnitude series relates to the nonlinear properties of the original time series, while the sign series relates to the linear properties, our findings suggest that the nonlinear properties of the heartbeat dynamics are more pronounced during REM sleep. Thus, the sign and the magnitude series provide information which is useful in distinguishing between the sleep stages.

Experimental evidence for phase synchronization transitions in the human cardiorespiratory system.

Transitions in the dynamics of complex systems can be characterized by changes in the synchronization behavior of their components. Taking the human cardiorespiratory system as an example and using an automated procedure for screening the synchrograms of 112 healthy subjects we study the frequency and the distribution of synchronization episodes under different physiological conditions that occur during sleep. We find that phase synchronization between heartbeat and breathing is significantly enhanced during non-rapid-eye-movement (non-REM) sleep (deep sleep and light sleep) and reduced during REM sleep. Our results suggest that the synchronization is mainly due to a weak influence of the breathing oscillator upon the heartbeat oscillator, which is disturbed in the presence of long-term correlated noise, superimposed by the activity of higher brain regions during REM sleep.

Orexin Receptor Antagonism, a New Sleep-Enabling Paradigm: A Proof-of-Concept Clinical Trial

The orexin system is a key regulator of sleep and wakefulness. In a multicenter, double‐blind, randomized, placebo‐controlled, two‐way crossover study, 161 primary insomnia patients received either the dual orexin receptor antagonist almorexant, at 400, 200, 100, or 50 mg in consecutive stages, or placebo on treatment nights at 1‐week intervals. The primary end point was sleep efficiency (SE) measured by polysomnography; secondary end points were objective latency to persistent sleep (LPS), wake after sleep onset (WASO), safety, and tolerability. Dose‐dependent almorexant effects were observed on SE, LPS, and WASO. SE improved significantly after almorexant 400 mg vs. placebo (mean treatment effect 14.4%; P < 0.001). LPS (–18 min (P = 0.02)) and WASO (–54 min (P < 0.001)) decreased significantly at 400 mg vs. placebo. Adverse‐event incidence was dose‐related. Almorexant consistently and dose‐dependently improved sleep variables. The orexin system may offer a new treatment approach for primary insomnia.

Quantification of tonic and phasic muscle activity in REM sleep behavior disorder.

Summary: REM sleep behavior disorder (RBD) is characterized by excessive tone of the chin muscle and limb movement during sleep. In the past, quantification of increased muscle tone in REM sleep has been performed visually, using no stringent criteria. The aim of this study was to develop an automatic analysis, allowing the quantification of muscle activity and its amplitude for all sleep stages, with a focus on REM sleep in patients with RBD. Forty-eight patients (27 male, 21 female) with RBD were included in the analysis. Twenty-one had idiopathic RBD; 28 had narcolepsy plus RBD. Twenty-five patients without confirmed sleep disorder served as control subjects. The amplitude of the EMG was generated from the difference of the upper and lower envelope of the mentalis muscle recordings. By smoothing the amplitude curve, a threshold curve was defined. Any muscle activity beyond the threshold curve was defined as motor activity. The means of the motor activity per second were summarized statistically and calculated for each sleep stage. Due to variable distribution of REM sleep, the latter was assigned to respective quartiles of the recorded night. Muscle activity was defined according to a histogram as short-lasting (<0.5 second) and long-lasting (>0.5 second) activity. No difference in the distribution of REM sleep/quartile and mean muscle tone throughout the sleep cycle could be found within the RBD groups and control subjects. Muscle activity was in the range of 200 ms. No clusters or regular distribution of muscle activity were found. Long muscle activity in the group with manifest clinical RBD was significantly higher than in control subjects, whereas it was nonsignificantly higher in subclinical RBD. The correlation between the frequency of long muscle activity in REM sleep and age was highly significant only for patients with idiopathic RBD. Automatic analysis of muscle activity in sleep is a reliable, easy method that may easily be used in the evaluation for REM sleep behavior disorder, creating indices of muscle activity similar to the indices for sleep apnea or PLMS. Together with the overt behavior, the analyses provides an important tool to get a deeper insight into the pathophysiology of RBD. Long movements appear to represent the motor disinhibition in REM sleep more distinct than short movements. The positive correlation of age and increased motor activity in REM sleep in idiopathic RBD highlights the idea of age dependant motor disinhibition as a continuum of a neurodegenerative disorder, which in narcolepsy patients with RBD only seems to happen as a single temporal event at onset of the disorder.

Diabetes Mellitus Prevalence and Control in Sleep-Disordered Breathing: The European Sleep Apnea Cohort (ESADA) Study

BACKGROUND OSA is associated with an increased risk of cardiovascular morbidity. A driver of this is metabolic dysfunction and in particular type 2 diabetes mellitus (T2DM). Prior studies identifying a link between OSA and T2DM have excluded subjects with undiagnosed T2DM, and there is a lack of population-level data on the interaction between OSA and glycemic control among patients with diabetes. We assessed the relationship between OSA severity and T2DM prevalence and control in a large multinational population. METHODS We performed a cross-sectional analysis of 6,616 participants in the European Sleep Apnea Cohort (ESADA) study, using multivariate regression analysis to assess T2DM prevalence according to OSA severity, as measured by the oxyhemoglobin desaturation index. Patients with diabetes were identified by previous history and medication prescription, and by screening for undiagnosed diabetes with glycosylated hemoglobin (HbA1c) measurement. The relationship of OSA severity with glycemic control was assessed in diabetic subjects. RESULTS T2DM prevalence increased with OSA severity, from 6.6% in subjects without OSA to 28.9% in those with severe OSA. Despite adjustment for obesity and other confounding factors, in comparison with subjects free of OSA, patients with mild, moderate, or severe disease had an OR (95% CI) of 1.33 (1.04-1.72), 1.73 (1.33-2.25), and 1.87 (1.45-2.42) (P < .001), respectively, for prevalent T2DM. Diabetic subjects with more severe OSA had worse glycemic control, with adjusted mean HbA1c levels 0.72% higher in patients with severe OSA than in those without sleep-disordered breathing (analysis of covariance, P < .001). CONCLUSIONS Increasing OSA severity is associated with increased likelihood of concomitant T2DM and worse diabetic control in patients with T2DM.

Sleep Apnea Screening by Autoregressive Models From a Single ECG Lead

This paper presents a method for obstructive sleep apnea (OSA) screening based on the electrocardiogram (ECG) recording during sleep. OSA is a common sleep disorder produced by repetitive occlusions in the upper airways and this phenomenon can usually be observed also in other peripheral systems such as the cardiovascular system. Then the extraction of ECG characteristics, such as the RR intervals and the area of the QRS complex, is useful to evaluate the sleep apnea in noninvasive way. In the presented analysis, 50 recordings coming from the apnea Physionet database were used; data were split into two sets, the training and the testing set, each of which was composed of 25 recordings. A bivariate time-varying autoregressive model (TVAM) was used to evaluate beat-by-beat power spectral densities for both the RR intervals and the QRS complex areas. Temporal and spectral features were changed on a minute-by-minute basis since apnea annotations where given with this resolution. The training set consisted of 4950 apneic and 7127 nonapneic minutes while the testing set had 4428 apneic and 7927 nonapneic minutes. The K-nearest neighbor (KNN) and neural networks (NN) supervised learning classifiers were employed to classify apnea and non apnea minutes. A sequential forward selection was used to select the best feature subset in a wrapper setting. With ten features the KNN algorithm reached an accuracy of 88%, sensitivity equal to 85%, and specificity up to 90%, while NN reached accuracy equal to 88%, sensitivity equal to 89% and specificity equal to 86%. In addition to the minute-by-minute classification, the results showed that the two classifiers are able to separate entirely (100%) the normal recordings from the apneic recordings. Finally, an additional database with eight recordings annotated as normal or apneic was used to test again the classifiers. Also in this new dataset, the results showed a complete separation between apneic and normal recordings.

Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis.

This study analyses two different methods to detect obstructive sleep apnea (OSA) during sleep time based only on the ECG signal. OSA is a common sleep disorder caused by repetitive occlusions of the upper airways, which produces a characteristic pattern on the ECG. ECG features, such as the heart rate variability (HRV) and the QRS peak area, contain information suitable for making a fast, non-invasive and simple screening of sleep apnea. Fifty recordings freely available on Physionet have been included in this analysis, subdivided in a training and in a testing set. We investigated the possibility of using the recently proposed method of empirical mode decomposition (EMD) for this application, comparing the results with the ones obtained through the well-established wavelet analysis (WA). By these decomposition techniques, several features have been extracted from the ECG signal and complemented with a series of standard HRV time domain measures. The best performing feature subset, selected through a sequential feature selection (SFS) method, was used as the input of linear and quadratic discriminant classifiers. In this way we were able to classify the signals on a minute-by-minute basis as apneic or nonapneic with different best-subset sizes, obtaining an accuracy up to 89% with WA and 85% with EMD. Furthermore, 100% correct discrimination of apneic patients from normal subjects was achieved independently of the feature extractor. Finally, the same procedure was repeated by pooling features from standard HRV time domain, EMD and WA together in order to investigate if the two decomposition techniques could provide complementary features. The obtained accuracy was 89%, similarly to the one achieved using only Wavelet analysis as the feature extractor; however, some complementary features in EMD and WA are evident.