Electrophysiological Brain-Cardiac Coupling in Train Drivers during Monotonous Driving

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on an individual's cardiac health as measured by heart rate variability (HRV). Sixty-three train drivers participated in the present study, and were required to complete a monotonous train driver simulator task. During this task, a 32 lead EEG and a three-lead electrocardiogram were recorded from each participant. In the present analysis, the low (LF) and high frequency (HF) HRV parameters were associated with delta (p < 0.05), beta (p = 0.03) and gamma (p < 0.001) frequency EEG variables. Further, total HRV was associated with gamma activity, while sympathovagal balance (i.e., LF:HF ratio) was best associated fronto-temporal delta activity (p = 0.02). HRV and EEG parameters appear to be coupled, with the parameters of the delta and gamma EEG frequency bands potentially being the most important to this coupling. These relationships provide insight into the impact of a monotonous task on the cardiac health of train drivers, and may also be indicative of strategies employed to combat fatigue or engage with the driving task

Obstructive sleep apnoea-related respiratory events and desaturation severity are associated with the cardiac response

Obstructive sleep apnoea (OSA) causes, among other things, intermittent blood oxygen desaturations, increasing the sympathetic tone. Yet the effect of desaturations on heart rate variability (HRV), a simple and noninvasive method for assessing sympathovagal balance, has not been comprehensively studied. We aimed to study whether desaturation severity affects the immediate HRV.MethodsWe retrospectively analysed the electrocardiography signals in 5-min segments (n=39 132) recorded during clinical polysomnographies of 642 patients with suspected OSA. HRV parameters were calculated for each segment. The segments were pooled into severity groups based on the desaturation severity (i.e.the integrated area under the blood oxygen saturation curve) and the respiratory event rate within the segment. Covariate-adjusted regression analyses were performed to investigate possible confounding effects.ResultsWith increasing respiratory event rate, the normalised high-frequency band power (HFNU) decreased from 0.517 to 0.364 (p&lt;0.01), the normalised low-frequency band power (LFNU) increased from 0.483 to 0.636 (p&lt;0.01) and the mean RR interval decreased from 915 to 869 ms (p&lt;0.01). Similarly, with increasing desaturation severity, the HFNUdecreased from 0.499 to 0.364 (p&lt;0.01), the LFNUincreased from 0.501 to 0.636 (p&lt;0.01) and the mean RR interval decreased from 952 to 854 ms (p&lt;0.01). Desaturation severity-related findings were confirmed by considering the confounding factors in the regression analyses.ConclusionThe short-term HRV response differs based on the desaturation severity and the respiratory event rate in patients with suspected OSA. Therefore, a more detailed analysis of HRV and desaturation characteristics could enhance OSA severity estimation

Sleep apnea-hypopnea quantification by cardiovascular data analysis

Sleep disorders are a major risk factor for cardiovascular diseases. Sleep apnea is the most common sleep disturbance and its detection relies on a polysomnography, i.e., a combination of several medical examinations performed during a monitored sleep night. In order to detect occurrences of sleep apnea without the need of combined recordings, we focus our efforts on extracting a quantifier related to the events of sleep apnea from a cardiovascular time series, namely systolic blood pressure (SBP). Physiologic time series are generally highly nonstationary and entrap the application of conventional tools that require a stationary condition. In our study, data nonstationarities are uncovered by a segmentation procedure which splits the signal into stationary patches, providing local quantities such as mean and variance of the SBP signal in each stationary patch, as well as its duration L. We analysed the data of 26 apneic diagnosed individuals, divided into hypertensive and normotensive groups, and compared the results with those of a control group. From the segmentation procedure, we identified that the average duration 〈L〉, as well as the average variance 〈σ2〉, are correlated to the apnea-hypoapnea index (AHI), previously obtained by polysomnographic exams. Moreover, our results unveil an oscillatory pattern in apneic subjects, whose amplitude S∗ is also correlated with AHI. All these quantities allow to separate apneic individuals, with an accuracy of at least 79%. Therefore, they provide alternative criteria to detect sleep apnea based on a single time series, the systolic blood pressure

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

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Continuing professional development:introducing the ERS International Certificate in Respiratory Sleep Medicine

Certification of specialists in respiratory sleep medicine for the purposes of continuing professional developmen

Dynamics of Sleep-Wake Transitions During Sleep

We study the dynamics of the awakening during the night for healthy subjects and find that the wake and the sleep periods exhibit completely different behavior: the durations of wake periods are characterized by a scale-free power-law distribution, while the durations of sleep periods have an exponential distribution with a characteristic time scale. We find that the characteristic time scale of sleep periods changes throughout the night. In contrast, there is no measurable variation in the power-law behavior for the durations of wake periods. We develop a stochastic model which agrees with the data and suggests that the difference in the dynamics of sleep and wake states arises from the constraints on the number of microstates in the sleep-wake system.Comment: Final form with some small corrections. To be published in Europhysics Letters, vol. 57, issue no. 5, 1 March 2002, pp. 625-63

Feasibility and diagnostic reliability of quantitative flow ratio in the assessment of non-culprit lesions in acute coronary syndrome

Several studies have demonstrated the feasibility and safety of hemodynamic assessment of non-culprit coronary arteries in setting of acute coronary syndromes (ACS) using fractional flow reserve (FFR) measurements. Quantitative flow ratio (QFR), recently introduced as angiography-based fast FFR computation, has been validated with good agreement and diagnostic performance with FFR in chronic coronary syndromes. The aim of this study was to assess the feasibility and diagnostic reliability of QFR assessment during primary PCI. A total of 321 patients with ACS and multivessel disease, who underwent primary PCI and were planned for staged PCI of at least one non-culprit lesion were enrolled in the analysis. Within this patient cohort, serial post-hoc QFR analyses of 513 non-culprit vessels were performed. The median time interval between primary and staged PCI was 49 [42-58] days. QFR in non-culprit coronary arteries did not change between acute and staged measurements (0.86 vs 0.87, p = 0.114), with strong correlation (r = 0.94, p ≤ 0.001) and good agreement (mean difference -0.008, 95%CI -0.013-0.003) between measurements. Importantly, QFR as assessed at index procedure had sensitivity of 95.02%, specificity of 93.59% and diagnostic accuracy of 94.15% in prediction of QFR ≤ 0.80 at the time of staged PCI. The present study for the first time confirmed the feasibility and diagnostic accuracy of non-culprit coronary artery QFR during index procedure for ACS. These results support QFR as valuable tool in patients with ACS to detect further hemodynamic relevant lesions with excellent diagnostic performance and therefore to guide further revascularisation therapy

A Secure Cloud-based Platform to Host Healthcare Applications

Digital technologies, such as Big Data analytics, artificial intelligence, cloud and high-performance computing are presenting new opportunities to transform healthcare systems, increase connectivity of hospitals and other providers, and therefore potentially and significantly improve patient care. However, such networked computing infrastructures also raise significant cybersecurity risks, especially in the healthcare domain, where protecting sensitive personal information is of paramount importance. Project ASCLEPIOS aims at strengthening the trust of users in cloud-based healthcare services by utilizing trusted execution environment and several modern cryptographic approaches such as attribute based encryption, searchable encryption, functional encryption to build a cloud-based e-health framework that protects users’ privacy, prevents both internal and external attacks, verifies the integrity of medical devices before application, and runs privacy-preserving data analytics on encrypted data. The project investigates modern encryption techniques and their combination in order to provide increased security of e-health applications that are then presented towards end-users utilizing a cloud-based platform. Although some topics such as security and privacy are already investigated through block-chain related technologies, it has been decided that the selected approaches would be more suitable for these particular challenges. In order to prototype its security services, ASCLEPIOS develops and deploys three large-scale healthcare demonstrators, provided by three leading hospitals from Europe. These demonstrators are rooted in the practice-based problems and applications provided by the project’s healthcare partners. The Amsterdam University Centers, University of Amsterdam, plans to improve stroke hyper-acute care through secure information sharing on a cloud computing platform to improve patient management. Additionally, they are also building prediction models to enable earlier discharge of patients from hospitals with lower risk factors. Charité Berlin plans to improve inpatient and outpatient sleep medication by remotely controlling the quality of the collected data and transferring it on-line for further analysis. Finally, the Norwegian Centre for e-health Research, University Hospital of North Norway is developing a system for privacy-preserving monitoring and benchmarking of antibiotics prescription of general practitioners. The common characteristics of these three scenarios are the increased demand for high levels of security in data transfer, storage and privacy preserving analytics on cloud infrastructures. In order deploy, operate and further develop these applications to increase their security with the ASCLEPIOS framework, a cloud computing testbed is being setup. The testbed uses state-of-the-art technologies for cloud application deployment and run-time orchestration in order to ensure the optimized deployment and execution of the demonstrator applications. As the data sources do not require the local execution (albeit in one case data may remain on the data source) of processing, there is no need for fog or edge computing, but the testbed is based on private OpenStack cloud computing infrastructures and utilizes the MiCADO framework which is compatible with different containers such as Docker and Kubernetes. The project started only recently, and currently it is in the early stages of systems design and specification. This presentation will provide a short introduction to the ASCLEPIOS project and its demonstrators and will present early results of the currently ongoing requirements specification and platform design processes

Algorithm for the classification of multi-modulating signals on the electrocardiogram

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a(j)(t), u(j)(t), o(j)(a(j)) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a(j)(t), u(j)(t) change the source into the periodic signal and τ(j) = o(j)(a(j)) confines its harmonics into a few instantaneous components at τ(j) being a common instant on two scales between t and τ(j). As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a(j)(t), a(j+1)(t) and u(j+1)(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings