Cowanburris2575

A system for automated annotation of selected signals from the polysomnogram (PSG) for the presence of apnoea and non-apnoea arousals is presented. Fifty nine time- and frequency-domain features were derived from the PSG for each 15 second epoch and after combining features from adjacent epochs, the feature information was processed with a bank of feed-forward neural networks that provided a probability estimate that each epoch was associated with an apnoea or non-apnoea arousal, or no-arousal. Data from the Physionet Computing in Cardiology Challenge 2018 was used to develop and test the system. Performance of the system was assessed using volume under the receiver operator characteristic surface (VUROS) as well as no-arousal specificity and arousal sensitivities. Using a bank of ten feed-forward neural networks with each network processing ±4 epochs of features and each used a single hidden layer of 20 units, the system achieved a VUROS of 0.73 with a specificity of 70%, a sensitivity of 75% for the apnoea arousals, and a sensitivity of 70% for the non-apnoea arousals.Plant gain quantifies the extent and rapidity with which arterial blood gases change following hypopneic or hyperpneic events. High plant gain, acting in concert with a highly collapsible upper airway and low arousal threshold, may contribute significantly towards increasing the severity of obstructive sleep apnea (OSA), even when controller gain is low. Elevated plant gain may be a manifestation of abnormal gas exchange resulting from ventilation-perfusion mismatch in the lungs. Using a mathematical model, we explore in this paper how ventilation-perfusion mismatch can affect plant gain, as well as the severity of OSA.In this paper, we explored the link between sleep apnoea and cardiovascular disease (CVD) using a time-series statistical measure of sleep apnoea-related oxygen desaturation. We compared the performance of a hypoxic measure derived from the polysomnogram with the Apnoea Hypopnoea Index (AHI) in predicting CVD mortality in patients of the Sleep Heart Health Study.We estimated the relative cumulative time of SpO2 below 90% (Tr90) using pulse oximetry signals from polysomnogram recordings as the hypoxic measure of desaturation patterns. Then, the survival curves for hypoxia quintiles were evaluated for the prediction of CVD mortality and were compared with the results using AHI for prediction. We also calculated the Cox hazard ratios for Tr90 and AHI. Our results show that the Tr90 was a better predictor of CVD mortality outcomes than AHI.We present an approach to quantifying nocturnal blood pressure (BP) variations that are elicited by sleep disordered breathing (SDB). A sample-by-sample aggregation of the dynamic BP variations during normal breathing and BP oscillations prompted by apnea episodes is performed. This approach facilitates visualization and analysis of BP oscillations. Preliminary results from analysis of a full night study of 7 SDB subjects (5 Male 2 Female, 52±5.6 yrs., Body Mass Index 36.4±7.4 kg/m2, Apnea-Hypopnea Index 69.1±26.8) are presented. this website Aggregate trajectory and quantitative values for changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP) concomitant with obstructive apnea episodes are presented. The results show 19.4 mmHg (15.3%) surge in SBP and 9.4 mmHg (13.6%) surge in DBP compared to their respective values during normal breathing (p less then 0.05). Further, the peak of the surge in SBP and DBP occurred about 9s and 7s, respectively, post the end of apnea events. The return of SBP and DBP to baseline values displays a decaying oscillatory pattern.Sleep apnea has a very high prevalence in the general population. Sleep apnea can be the cause for cardiovascular disorders. An increased risk for suffering from hypertension, stroke, and myocardial infarction had been shown in large studies, like the Sleep Heart Health Study. Sleep related breathing disorders and sleep apnea had been diagnosed in sleep laboratories with polysomnography in the past. Today in view of the high prevalence of sleep disordered breathing, home sleep apnea testing (HSAT) has become the accepted test for the diagnosis of sleep apnea, if there are no other comorbidities, and if a high pretest probability was confirmed by a sleep physician. For home sleep apnea testing, the number of sensors needed should be reduced. Some methods use indirect means to derive features to detect sleep apnea and hypopnea events. A very well developed method is peripheral arterial tonometry (PAT). This method records the pulse wave on a finger and derives sleep and sleep apnea feature. The PAT method has been tested under many conditions. As an indirect method, it was long seen as a limitation that obstructive and central sleep apnea events could not be distinguished. A new multicenter trial was set up to develop algorithms, which could distinguish central and obstructive apnea events with sufficient accuracy.This document presents a new complete standalone system for a recognition of sleep apnea using signals from the pressure sensors placed under the mattress. The developed hardware part of the system is tuned to filter and to amplify the signal. Its software part performs more accurate signal filtering and identification of apnea events. The overall achieved accuracy of the recognition of apnea occurrence is 91%, with the average measured recognition delay of about 15 seconds, which confirms the suitability of the proposed method for future employment. The main aim of the presented approach is the support of the healthcare system with the cost-efficient tool for recognition of sleep apnea in the home environment.Usual care regarding vasopressor (VP) initiation is ill-defined. We aimed to further validate a quantitative model for usual care in the Emergency Department (ED) regarding the timing of VP initiation in sepsis. We retrospectively studied a cohort of adult critically-ill ED patients who also received antibiotics in the ED. We applied a multivariable model previously developed from another patient cohort which distinguishes between time points at which patients were or were not subsequently started on a continuous VP infusion. The model has six independently significant predictors (respiratory rate, Glasgow Coma Scale score, systolic blood pressure, SpO2, administered intravenous fluids, and elapsed time). The outcome was initiation of VP infusion, either within the ED or within 6 hours after leaving the ED. We applied the model to all time points, beginning when all model input parameters were first available for a given patient, and ending when either VP were first started, or the patient left the ED. Out of 55,963 adult ED patients during the two-year study interval, we identified 1,629 who met our inclusion criteria.