Automatic analysis of systolic, diastolic and mean blood pressure of continuous measurement before, during and after sleep arousals in polysomnographic overnight recordings

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This paper deals with a detailed examination of sleep arousal events and the corresponding changes of systolic, diastolic and mean blood pressure. Arousals are short awakening events during sleep which do not become noticeable for the sleeping person. But the organism increases vital parameters, e.g. the blood pressure. The recreative sleep is disturbed, and the risk factor for cardiovascular diseases rises significantly. Impact on the continuous measured blood pressure for two arousal groups named spontaneous and non spontaneous arousals will be investigated. Polysomnographic recordings of patients suffering from sleep apnoea and a healthy control group will be examined. Using averaged blood pressure curves and a high time resolution, the courses are investigated in more detail than before. The results show an increasing slope a few seconds before and possible pressure minima a few seconds after the beginning of the arousal.EC/FP6/018474-2/EU/Dynamic analysis of physiological Networks/Daphne

A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals

Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often restrict users' permissions to install software. This paper introduces a new web-based application for interactive biosignal visualization and assessment. A focus has been placed on performance to allow for handling files of any size. The proposed solution can load local and remote files. It parses data locally on the client, and harmonizes channel labels. The data can then be scored, annotated, pseudonymized and uploaded to a clinical data management system for further analysis. The data and all actions can be interactively shared with a second party. This lowers the barrier to quickly visually examine data, collaborate and make informed decisions

Sleep apnea-hypopnea quantification by cardiovascular data analysis

Sleep apnea is the most common sleep disturbance and it is an important risk factor for cardiovascular disorders. Its detection relies on a polysomnography, a combination of diverse exams. In order to detect changes due to sleep disturbances such as sleep apnea occurrences, without the need of combined recordings, we mainly analyze systolic blood pressure signals (maximal blood pressure value of each beat to beat interval). Nonstationarities in the data are uncovered by a segmentation procedure, which provides local quantities that are correlated to apnea-hypopnea events. Those quantities are the average length and average variance of stationary patches. By comparing them to an apnea score previously obtained by polysomnographic exams, we propose an apnea quantifier based on blood pressure signal. This furnishes an alternative procedure for the detection of apnea based on a single time series, with an accuracy of 82%

Automatic validation and quality based readjustment of manually scored EEG arousal

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.A knowledge of arousals during sleep is important to attain a deeper understanding regarding cardiovascular diseases. Manual scoring is time consuming and not always accurate. Automatic approaches are even worse inter alia due to inaccurate learning data. This paper presents an algorithm to improve the accuracy of manually scored data. Also a measure of quality is introduced to judge the automatically estimated results.EC/FP6/018474-2/EU/Dynamic analysis of physiological Networks/Daphne

Sleep heart rate variability analysis and k-nearest neighbours classification of primary insomnia

The Heart Rate Variability (HRV) of many sleep disorders shows an alteration of the sympathovagal balance of the Autonomous Nervous System (ANS). Primary insomnia refers to the difficulty in initiating or maintaining sleep that is not caused by other illnesses or substances. The HRV of primary insomnia shows inconsistent findings although it is believed to impair the HRV variables. This study compares the HRV changes during different sleep stages and evaluates the k-nearest neighbours (kNN) classifier using the HRV features for primary insomnia classification. The time and frequency HRV variables were extracted from sleep ECG signals of 10 primary insomnia patients and 10 healthy controls during four sleep stages - N1, N2, N3 and REM. The Mann-Whitney U-test was conducted to evaluate the existence of statistical significant differences between the two groups at different sleep stages. The kNN classifier was adapted for the classification tool. Only the LF index of HRV was significantly higher in the primary insomnia patients compared to the healthy subjects. The classification accuracy of kNN was at 75% when both the HRV time and frequency variables were accounted as inputs to the classifier

Management of Obstructive Sleep Apnea in Patients With Heart Failure

Sleep apnea is traditionally classified as obstructive sleep apnea (OSA), which occurs when the upper airway collapses due to the relaxation of oropharyngeal musculature, and central sleep apnea occurs when the brainstem cannot stimulate breathing. Most sleep apnea in patients with heart failure (HF) results from coexisting OSA and central sleep apnea (CSA), or complex sleep apnea syndrome. OSA and CSA are common in HF and can be involved in its progression by exposure to the heart to intermittent hypoxia, increased preload and afterload, activating sympathetic, and decreased vascular endothelial function. A majority of treatments have been investigated in patients with CSA and HF; however, less or short-term randomized trials demonstrated whether treating OSA in patients with HF could improve morbidity and mortality. OSA could directly influence the patient's recovery. This review will focus on past and present studies on the various therapies for OSA in patients with HF and summarize CSA treatment options for reasons of reference and completeness. More specifically, the treatment covered include surgical and non-surgical treatments and reported the positive and negative consequences for these treatment options, highlighting possible implications for clinical practice and future research directions

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.Peer Reviewe

Sleep as a Novel Biomarker and a Promising Therapeutic Target for Cerebral Small Vessel Disease: A Review Focusing on Alzheimer’s Disease and the Blood-Brain Barrier

Cerebral small vessel disease (CSVD) is a leading cause of cognitive decline in elderly people and development of Alzheimer’s disease (AD). Blood–brain barrier (BBB) leakage is a key pathophysiological mechanism of amyloidal CSVD. Sleep plays a crucial role in keeping health of the central nervous system and in resistance to CSVD. The deficit of sleep contributes to accumulation of metabolites and toxins such as beta-amyloid in the brain and can lead to BBB disruption. Currently, sleep is considered as an important informative platform for diagnosis and therapy of AD. However, there are no effective methods for extracting of diagnostic information from sleep characteristics. In this review, we show strong evidence that slow wave activity (SWA) (0–0.5 Hz) during deep sleep reflects glymphatic pathology, the BBB leakage and memory deficit in AD. We also discuss that diagnostic and therapeutic targeting of SWA in AD might lead to be a novel era in effective therapy of AD. Moreover, we demonstrate that SWA can be pioneering non-invasive and bed–side technology for express diagnosis of the BBB permeability. Finally, we review the novel data about the methods of detection and enhancement of SWA that can be biomarker and a promising therapy of amyloidal CSVD and CSVD associated with the BBB disorders. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Sleep as a Novel Biomarker and a Promising Therapeutic Target for Cerebral Small Vessel Disease: A Review Focusing on Alzheimer’s Disease and the Blood-Brain Barrier

Cerebral small vessel disease (CSVD) is a leading cause of cognitive decline in elderly people and development of Alzheimer’s disease (AD). Blood–brain barrier (BBB) leakage is a key pathophysiological mechanism of amyloidal CSVD. Sleep plays a crucial role in keeping health of the central nervous system and in resistance to CSVD. The deficit of sleep contributes to accumulation of metabolites and toxins such as beta-amyloid in the brain and can lead to BBB disruption. Currently, sleep is considered as an important informative platform for diagnosis and therapy of AD. However, there are no effective methods for extracting of diagnostic information from sleep characteristics. In this review, we show strong evidence that slow wave activity (SWA) (0–0.5 Hz) during deep sleep reflects glymphatic pathology, the BBB leakage and memory deficit in AD. We also discuss that diagnostic and therapeutic targeting of SWA in AD might lead to be a novel era in effective therapy of AD. Moreover, we demonstrate that SWA can be pioneering non-invasive and bed–side technology for express diagnosis of the BBB permeability. Finally, we review the novel data about the methods of detection and enhancement of SWA that can be biomarker and a promising therapy of amyloidal CSVD and CSVD associated with the BBB disorders.Russian Science FoundationRussian Foundation for Basic ResearchMinistry of Science and Higher Education of the Russian FederationPeer Reviewe