Biomedical subjects

Thomas Penzel

Publications and source records attributed to Thomas Penzel.

12 recordsLinked to original sources

PubMed2006

ENN-ICS--implementation and evaluation of a multilingual learning management system for sleep medicine in Europe.

A new web based network aims at the improvement of health care in Europe by integrating advanced e-learning and e-publishing technologies for the training of medical doctors, nurses, and students. The field of application is sleep physiology and sleep medicine. Based on a multilingual, multimedia communication system, ENN-ICS Centre offers direct access to medical information for users, i.e healthcare professionals and citizens, in Europe and worldwide. The use of XML supports the development of media independent contents for multiple target groups. Editorial and distributive processes are supported by customized central editorial, content management and learning management systems (CMS, LMS). ENN-ICS e-health services are evaluated by selected user groups in North, Middle and Southern Europe using reliable and scientifically accepted validation instruments. The compliance with essential quality requirements and criteria is tested and verified by using online questionnaires based on the DISCERN questionnaire for evaluating patient information, the HON principles for health-related websites and the GMDS catalogue of quality criteria for electronic publications in medicine. The system architecture and its exemplary applications can be used as a model for future e-health services dealing with neurological and other medical topics.

Europe↗

PubMed2005

An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24 x 7 utilizing the Siesta database.

To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel. It adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system (Somnolyzer 24 x 7) consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, SEMs and REMs), a feature matrix plausibility check, a classifier designed as an expert system, a rule-based smoothing procedure for the start and the end of stages REM, and finally a statistical comparison to age- and sex-matched normal healthy controls (Siesta Spot Report). The expert system considers different prior probabilities of stage changes depending on the preceding sleep stage, the occurrence of a movement arousal and the position of the epoch within the NREM/REM sleep cycles. Moreover, results obtained with and without using the chin EMG signal are combined. The Siesta polysomnographic database (590 recordings in both normal healthy subjects aged 20-95 years and patients suffering from organic or nonorganic sleep disorders) was split into two halves, which were randomly assigned to a training and a validation set, respectively. The final validation revealed an overall epoch-by-epoch agreement of 80% (Cohen's kappa: 0.72) between the Somnolyzer 24 x 7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen's kappa: 0.68) between two human experts scoring the same dataset. Two Somnolyzer 24 x 7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen's kappa: 0.991), which confirmed that the variability induced by the quality control procedure, whereby approximately 1% of the epochs (in 9.5% of the recordings) are changed, can definitely be neglected. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24 x 7 and demonstrated its applicability in clinical routine and sleep studies.

Adult↗

PubMed2005

Perception of sleep: subjective versus objective sleep parameters in patients with Parkinson's disease in comparison with healthy elderly controls. Sleep perception in Parkinson's disease and controls.

INTRODUCTION: Subjective sleep perception, as measured against objective parameters such as those obtained by polysomnography, have not been examined thoroughly to date. Little is known about subjective sleep perception in patients with chronic somatic diseases. PATIENTS AND METHODS: Patients with Parkinson's disease (PD) and healthy elderly controls filled in a sleep log over 14 days, which included a self-rating questionnaire concerning sleep and quality of time awake, sleep times and somatic complaints. All participants underwent polysomnography in the sleep lab on nights 7 and 8, and slept all other nights at home. RESULTS: Seventeen patients with PD (64 +/- 6 years, 6 female, Hoehn and Yahr median = 2), and 62 healthy controls of the same age without sleep disturbances (64 +/- 8 years, 36 female) were included. Patients with PD showed reduced subjective sleep (p = 0.001) and quality of time awake (p = 0.02), decreased sleep duration (p = 0.01) and reduced sleep efficiency (p = 0.004) compared with the controls. Subjective sleep efficiency at home was no different from that in the sleep lab for both groups. Patients with PD reported more somatic complaints (p = 0.001) than controls but did not show a firstnight effect. CONCLUSION: In summary, patients with PD have subjectively and objectively disturbed sleep as compared to healthy controls of the same age. However, they may not rate this poor sleep as much changed from their baseline sleep at home, and they have more somatic complaints. Increasing sleep efficiency might be of importance in PD patients, as it shows an association with subjective quality of time awake in the morning.

Aged↗

PubMed2005

Nonrandom variability of respiration during sleep in healthy humans.

STUDY OBJECTIVES: Breath-to-breath variability is not purely random but is, instead, characterized by correlations on short- and long-term scales. Short-term correlations might reflect intact metabolic-control mechanisms. To investigate whether the higher variability of breathing during rapid eye movement (REM) compared to non-REM (NREM) sleep is of random or nonrandom nature--reflecting an altered respiratory control--short-term and long-term correlations of respiratory drive and timing were determined. DESIGN: A full-night polysomnogram with a pneumotachograph attached to a full-face mask was performed. For each breath during NREM and REM sleep, respiratory components were analyzed based on the quantitative airflow. SETTING: Data collection took place in the sleep laboratory. PARTICIPANTS: Twenty-nine healthy subjects (age, 25.8 +/- 3.1 years). MEASUREMENTS AND RESULTS: Long-term correlations are practically absent in respiratory timing and drive components during NREM sleep, whereas they are present during REM sleep. Short-term correlations are present in respiratory drive, tidal volume, and minute ventilation during both NREM and REM sleep. In all timing components, additional short-term correlations are absent. CONCLUSION: We conclude that from NREM to REM sleep, short-term regulation of respiratory drive remains strongly metabolically controlled and clearly different from the short-term regulation of the rhythm-generating function. Regulation of respiratory timing and drive during REM sleep is characterized by additional long-term correlations. We speculate that this is the result of cortical influences during phasic REM sleep. Thus, the variability of breathing during REM sleep contains a nonrandom component, such that breathing components remain dependent upon each other even with large time lags between components.

Adult↗

PubMed2004

Common scale-invariant patterns of sleep-wake transitions across mammalian species.

Although mammals of different species have different sleep patterns, brief sleep-wake transitions commonly are observed across species and appear to occur randomly throughout the sleeping period. The dynamical patterns and functions of these brief awakenings from sleep are not well understood, and they often are viewed as disruptions (random or pathologic) of the sleep process. In this article, we hypothesize that brief awakenings from sleep may reflect aspects of the endogenous sleep control mechanism and thus may exhibit certain robust dynamical patterns across species. We analyze sleep recordings from mice, rats, cats, and humans, and we compare the distributions of sleep and wake episode durations. For all four species, we find that durations of brief wake episodes during the sleep period exhibit a scale-free power-law behavior with an exponent alpha that remains the same for all species (alpha approximately equal to 2.2). In contrast, sleep episode durations for all four species follow exponential distributions with characteristic time scales, which change across species in relation to body mass and metabolic rate. Our findings suggest common dynamical features of brief awakenings and sleep durations across species and may provide insights into the dynamics of the neural circuits controlling sleep.

Animals↗

PubMed2004

Automated detection of obstructive sleep apnoea at different time scales using the electrocardiogram.

An automated classification algorithm is presented which processes short-duration epochs of surface electrocardiogram data derived from polysomnography studies, and determines whether an epoch is from a period of sleep disordered respiration (SDR) or normal respiration (NR). The epoch lengths considered were 15, 30, 45, 60, 75, and 90 s. Epochs were labeled as 'NR' or 'SDR' by a human expert, based on standard polysomnography interpretation rules. The automated classification algorithm was trained and tested on a database of 70 overnight ECG recordings from subjects with and without obstructive sleep apnoea syndrome (35 used for training, 35 for independent validation). Depending on the epoch length, the classifier correctly labeled between 87% (15 s epochs) and 91% (60 s epochs) of the epochs in the test set. Accuracy was lowest for the shortest (15 s) and longest (90 s) epoch lengths, but the analysis was relatively insensitive to choice of epoch length. The classifications from these epochs were combined to form an overall summary measure of minutes-of-SDR, allowing per-subject classification.

Adult↗

PubMed2004

Peripheral arterial tonometry, oximetry and actigraphy for ambulatory recording of sleep apnea.

Autonomous nervous functions change with sleep stages and show characteristic changes associated with sleep disorders. Therefore, continuous monitoring of autonomous nervous functions during sleep can be used for diagnostic purposes. Recently, the peripheral arterial tonometry (PAT) has been introduced to determine peripheral arterial vascular tone on the finger being determined by sympathetic activity. We investigate a new ambulatory recording system which uses PAT, oximetry and actigraphy (Watch-PAT) in order to detect sleep apnea and arousal. The Watch-PAT is battery operated and attached to the wrist and has two finger sensors. Twenty-one patients with suspected sleep apnea were recorded with cardiorespiratory polysomnography and the new system in parallel. Seventeen recordings could be evaluated. The correlation for the apnea/hypopnea index derived from the sleep laboratory and the respiratory disturbance index derived from the Watch-PAT was r = 0.89 (p < 0.01) and between arousals and the respiratory disturbance index was r = 0.77 (p < 0.01). The correlation for the total sleep time compared between the two systems was r = 0.15 (n.s.). The Watch-PAT detects apneas and hypopneas with a reasonable reliability and it is very sensitive to arousals. The number of Watch-PAT events lies between the sum of apneas plus hypopneas and arousals. Arousals are not unique to apnea events and therefore the specifity of the Watch-PAT is limited. In conclusion, the Watch-PAT is well suited to perform therapy control studies in patients suffering from sleep apnea and being treated.

Adult↗

PubMed2003

Effect of nasal continuous positive airway pressure treatment on blood pressure in patients with obstructive sleep apnea.

BACKGROUND: There is increasing evidence that obstructive sleep apnea (OSA) is an independent risk factor for arterial hypertension. Because there are no controlled studies showing a substantial effect of nasal continuous positive airway pressure (nCPAP) therapy on hypertension in OSA, the impact of treatment on cardiovascular sequelae has been questioned altogether. Therefore, we studied the effect of nCPAP on arterial hypertension in patients with OSA. METHODS AND RESULTS: Sixty consecutive patients with moderate to severe OSA were randomly assigned to either effective or subtherapeutic nCPAP for 9 weeks on average. Nocturnal polysomnography and continuous noninvasive blood pressure recording for 19 hours was performed before and with treatment. Thirty two patients, 16 in each group, completed the study. Apneas and hypopneas were reduced by approximately 95% and 50% in the therapeutic and subtherapeutic groups, respectively. Mean arterial blood pressure decreased by 9.9+/-11.4 mm Hg with effective nCPAP treatment, whereas no relevant change occurred with subtherapeutic nCPAP (P=0.01). Mean, diastolic, and systolic blood pressures all decreased significantly by approximately 10 mm Hg, both at night and during the day. CONCLUSIONS: Effective nCPAP treatment in patients with moderate to severe OSA leads to a substantial reduction in both day and night arterial blood pressure. The fact that a 50% reduction in the apnea-hypopnea index did not result in a decrease in blood pressure emphasizes the importance of highly effective treatment. The drop in mean blood pressure by 10 mm Hg would be predicted to reduce coronary heart disease event risk by 37% and stroke risk by 56%.

Blood Pressure↗

PubMed2003

Dynamics of heart rate and sleep stages in normals and patients with sleep apnea.

Sleep is not just the absence of wakefulness but a regulated process with an important restorative function. Based on electroencephalographic recordings and characteristic patterns and waveforms we can distinguish wakefulness and five sleep stages grouped into light sleep, deep sleep, and rapid-eye-movement (REM) sleep. In order to explore the functions of sleep and sleep stages, we investigated the dynamics of sleep stages over the night and of heart-rate variability during the different sleep stages. Sleep stages and intermediate wake states have different distributions of their duration and this allows us to create a model for the temporal sequence of sleep stages and wake states. Heart rate is easily accessed with a high precision by the recording and analysis of the electrocardiogram (ECG). Heart-rate regulation is part of the autonomous nervous system and sympathetic tone is strongly influenced by the sleep stages.

Heart Rate↗

PubMed2003

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.

Adult↗

PubMed2002

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.

Biophysics↗

PubMed2000

Computer based sleep recording and analysis.

Sleep analysis is based on polysomnography. Modern polysomnographic systems are computer based. Visual and automatic analysis of sleep and respiration is supported by most computer based systems. Four functions can be distinguished in computer based polysomnography: recording, documentation during the recording, automatic and visual analysis and report generation. This review compiles the minimal requirements for digital sleep recording, documentation, analysis and reporting. The basic principles of automatic sleep analysis are reported. The requirements and the basic principles for the analysis of non-electroencephalography (EEG) signals, such as respiration, snoring, oxygen saturation, electrocardiography (ECG) and options are reported. New developments in sleep EEG processing are discussed to enlighten how computer based sleep analysis can add quantative parameters to the rules for visual sleep staging established by Rechtschaffen and Kales 30 years ago. This helps to extend our understanding of sleep.

Journal Article↗