1,288 research outputs found

    Improving outcomes in interstitial lung disease through the application of bioinformatics and systems biology

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    Idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are two distinct respiratory diseases whose features including pathogenesis and progression are not fully understood. However, both clinicians utilise changes in serial pulmonary function measurements to gain an insight into disease severity and control. More accurate prediction of disease progression would be beneficial, particularly for IPF given the variability in its clinical course as an unknown factor at the time of diagnosis. Home-based, real-time monitoring of disease progression by spirometry has provided an opportunity to optimise the delivery of treatment and reduce the length of clinical trials. Therefore, the potential to understand the mechanisms underlying disease progression and generate effective treatment has been improved. In light of this, the motivation for this project is to understand the mathematical features within daily pulmonary function time series generated by IPF patients. Hopefully, statistical models of pulmonary function time series would aid the identification of significant clinical events such as acute exacerbation. The mathematical techniques used to identify potentially important features within pulmonary function time series involved the autocorrelation function, critical transitions and detrended fluctuation analysis (DFA). Temporal properties, such as the serial correlation, abrupt changes in trends and complexity, were assessed using time series from the PROFILE clinical trial and London COPD cohort. Forced vital capacity (FVC) measurements were found to be correlated to the previous day’s reading which may inform the sampling rate of lung function during clinical trials. The presence of short-term memory within FVC time series will influence the management of missing data within clinical trials, particularly methods of imputation. Also, FVC time series’ exhibit long-term memory and adaptability supporting the role of FVC as a surrogate marker for IPF disease progression.Open Acces

    Dynamics of Snoring Sounds and Its Connection with Obstructive Sleep Apnea

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    Snoring is extremely common in the general population and when irregular may indicate the presence of obstructive sleep apnea. We analyze the overnight sequence of wave packets --- the snore sound --- recorded during full polysomnography in patients referred to the sleep laboratory due to suspected obstructive sleep apnea. We hypothesize that irregular snore, with duration in the range between 10 and 100 seconds, correlates with respiratory obstructive events. We find that the number of irregular snores --- easily accessible, and quantified by what we call the snore time interval index (STII) --- is in good agreement with the well-known apnea-hypopnea index, which expresses the severity of obstructive sleep apnea and is extracted only from polysomnography. In addition, the Hurst analysis of the snore sound itself, which calculates the fluctuations in the signal as a function of time interval, is used to build a classifier that is able to distinguish between patients with no or mild apnea and patients with moderate or severe apnea

    Continuous Multi-Parameter Heart Rate Variability Analysis Heralds Onset of Sepsis in Adults

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    BACKGROUND: Early diagnosis of sepsis enables timely resuscitation and antibiotics and prevents subsequent morbidity and mortality. Clinical approaches relying on point-in-time analysis of vital signs or lab values are often insensitive, non-specific and late diagnostic markers of sepsis. Exploring otherwise hidden information within intervals-in-time, heart rate variability (HRV) has been documented to be both altered in the presence of sepsis, and correlated with its severity. We hypothesized that by continuously tracking individual patient HRV over time in patients as they develop sepsis, we would demonstrate reduced HRV in association with the onset of sepsis. METHODOLOGY/PRINCIPAL FINDINGS: We monitored heart rate continuously in adult bone marrow transplant (BMT) patients (n = 21) beginning a day before their BMT and continuing until recovery or withdrawal (12+/-4 days). We characterized HRV continuously over time with a panel of time, frequency, complexity, and scale-invariant domain techniques. We defined baseline HRV as mean variability for the first 24 h of monitoring and studied individual and population average percentage change (from baseline) over time in diverse HRV metrics, in comparison with the time of clinical diagnosis and treatment of sepsis (defined as systemic inflammatory response syndrome along with clinically suspected infection requiring treatment). Of the 21 patients enrolled, 4 patients withdrew, leaving 17 patients who completed the study. Fourteen patients developed sepsis requiring antibiotic therapy, whereas 3 did not. On average, for 12 out of 14 infected patients, a significant (25%) reduction prior to the clinical diagnosis and treatment of sepsis was observed in standard deviation, root mean square successive difference, sample and multiscale entropy, fast Fourier transform, detrended fluctuation analysis, and wavelet variability metrics. For infected patients (n = 14), wavelet HRV demonstrated a 25% drop from baseline 35 h prior to sepsis on average. For 3 out of 3 non-infected patients, all measures, except root mean square successive difference and entropy, showed no significant reduction. Significant correlation was present amongst these HRV metrics for the entire population. CONCLUSIONS/SIGNIFICANCE: Continuous HRV monitoring is feasible in ambulatory patients, demonstrates significant HRV alteration in individual patients in association with, and prior to clinical diagnosis and treatment of sepsis, and merits further investigation as a means of providing early warning of sepsis

    Heart Rate Variability from Wearable Photoplethysmography Systems: Implications in Sleep Studies at High Altitude

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    The interest in photoplethysmography (PPG) for sleep monitoring is increasing because PPG may allow assessing heart rate variability (HRV), which is particularly important in breathing disorders. Thus, we aimed to evaluate how PPG wearable systems measure HRV during sleep at high altitudes, where hypobaric hypoxia induces respiratory disturbances. We considered PPG and electrocardiographic recordings in 21 volunteers sleeping at 4554 m a.s.l. (as a model of sleep breathing disorder), and five alpine guides sleeping at sea level, 6000 m and 6800 m a.s.l. Power spectra, multiscale entropy, and self-similarity were calculated for PPG tachograms and electrocardiography R-R intervals (RRI). Results demonstrated that wearable PPG devices provide HRV measures even at extremely high altitudes. However, the comparison between PPG tachograms and RRI showed discrepancies in the faster spectral components and at the shorter scales of self-similarity and entropy. Furthermore, the changes in sleep HRV from sea level to extremely high altitudes quantified by RRI and PPG tachograms in the five alpine guides tended to be different at the faster frequencies and shorter scales. Discrepancies may be explained by modulations of pulse wave velocity and should be considered to interpret correctly autonomic alterations during sleep from HRV analysis

    Advanced analyses of physiological signals and their role in Neonatal Intensive Care

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    Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity

    Heart Rate Fractality Disruption as a Footprint of Subthreshold Depressive Symptoms in a Healthy Population

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    Psychopathology (and depression in particular) is a cardiovascular risk factor independent from any co-occurring pathology. This link is traced back to the mind-heart-body connection, whose underlying mechanisms are still not completely known. To study psychopathology in relation to the heart, it is necessary to observe the autonomic nervous system, which mediates among the parts of that connection. Its gold standard of evaluation is the study of heart rate variability (HRV). To investigate whether any association exists between the HRV parameters and sub-threshold depressive symptoms in a sample of healthy subjects

    Statistical Analysis of Seismicity Catalog of Alaska: The mysteries of the timeseries

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    Abstract:This dissertation presents the results of applying three independent statistical techniques on the seismic catalog of Alaska and Aleutian subduction zone. I perform Visibility Graph Analysis and Multifractal Detrended Fluctuation Analysis respectively on the seismic catalogs of several defined seismogenic zones in surface and depth. Forecasting earthquake hazard is based on the assumption that the Gutenberg-Richter relation represents the size distribution of future earthquakes and we show that the series produced by these methods have properties with close correlation with the b-value of the Gutenberg-Richter law. Visibility graph analysis basically maps a time series into the networks of nodes and connection and we want to show that produced network keeps a relationship with seismic characteristics of the region. Same goes for the multifractal detrended fluctuation analysis which studies the multifractality of the seismic catalogs as magnitude time series. I am also trying to improve the spatial information of the catalog using the Condensation method based on the location error. It will produce a new catalog that differs with the original one by the new assigned weight to the events according to their accuracy relative to the neighboring events. Using this statistical method will contribute to the discovery of previously unknown active structures and a better understanding of seismic hazards in Alaska
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