453 research outputs found

    Aspects of Pacemakers

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    Outstanding steps forward were made in the last decades in terms of identification of endogenous pacemakers and the exploration of their controllability. New "artifical" devices were developed and are now able to do much more than solely pacemaking of the heart. In this book different aspects of pacemaker - functions and interactions, in various organ systems were examined. In addition, various areas of application and the potential side effects and complications of the devices were discussed

    INTRODUCTION TO HUMAN PHYSIOLOGY FOR MEDICAL STUDENTS

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    With recent advances in the fielded of human physiology, it has become urgent to provide an up to date review in the subject of human physiology.This book to help medical student in understanding modern human physiology. It presents the whole subject in brief comprehensive and up to date form.I hope this book will be a real help to undergraduate medical students, as well as to postgraduate and candidates of higher degree, in the field of human physiology

    Universal features of correlated bursty behaviour

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    Inhomogeneous temporal processes, like those appearing in human communications, neuron spike trains, and seismic signals, consist of high-activity bursty intervals alternating with long low-activity periods. In recent studies such bursty behavior has been characterized by a fat-tailed inter-event time distribution, while temporal correlations were measured by the autocorrelation function. However, these characteristic functions are not capable to fully characterize temporally correlated heterogenous behavior. Here we show that the distribution of the number of events in a bursty period serves as a good indicator of the dependencies, leading to the universal observation of power-law distribution in a broad class of phenomena. We find that the correlations in these quite different systems can be commonly interpreted by memory effects and described by a simple phenomenological model, which displays temporal behavior qualitatively similar to that in real systems

    Intelligent Biosignal Processing in Wearable and Implantable Sensors

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    This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

    Predicting Death in Chronic Heart Failure: Electrocardiographic, Autonomic and Neuroendocrine Risk Assessment

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    Chronic heart failure is a common condition with an adverse prognosis. Despite optimal treatment, ambulant patients with mild symptoms have an annual mortality of more than 15%. Clinical, exercise, echocardiographic and haemodynamic variables are known to carry prognostic information, but accurate identification of those most likely to die remains difficult. This work assessed abnormalities of ventricular activation and repolarisation respectively using the signal-averaged electrocardiogram and interlead QT interval variability of the standard 12-lead electrocardiogram. Disordered autonomic function is common in cardiac failure. This was assessed by 24 hour heart rate variability and baroreflex sensitivity. Plasma noradrenaline and plasma atrial and brain natriuretic peptide levels were used to assess neuroendocrine activation, a hallmark of chronic heart failure. These measures were determined prospectively and compared with known prognostic variables in a chronic heart failure population. Original Hypotheses 1. Sudden cardiac death in patients with heart failure is caused predominantly by malignant ventricular arrhythmias. These may be predicted by non-invasive markers of the arrhythmogenic substrate i.e. signal-averaged ECG, QT dispersion; triggers i.e. non-sustained ventricular tacycardia, and autonomic modulators i.e. heart rate variability and baroreflex sensitivity. This assessment will provide additional independent prognostic information on mortality risk in patients with chronic heart failure. 2. Markers of neuroendocrine activation and autonomic dysfunction would predict progression of chronic heart failure, and all-cause and progressive heart failure death. Discussion Chronic heart failure is a common, growing and major public health care burden. Identifying high-risk patients suitable for aggressive intervention, optimisation of treatment and prevention of death is of great importance. Despite extensive study by many investigators, identification of those patients who are most likely to deteriorate and die remains difficult. In this well-characterised cohort of patients with chronic heart failure, neuroendocrine activation assessed by plasma BNP or plasma Noradrenaline predicted cardiovascular death. This information was additive to and independent of other powerful prognostic variables including NYHA class, age, left ventricular ejection fraction, peak VO2 and presence/absence of bundle branch block. However, plasma BNP may be measured from a simple venous blood sample, and has been proven to be stable at room temperature over 72 hours. It is inexpensive, and requires no specialised equipment at the bedside. Direct assay kits are now available which both simplify and lessen the cost of its measurement. This has implication for its more widespread use. Interestingly, a positive SAECG, the presence of non-sustained ventricular tachycardia and depressed baroreflex sensitivity all identified a patient cohort at high risk of sudden death. Linking this data with the prognostic importance of depressed baroreflex sensitivity in the study cohort with recent data on "electrical storms" in patients with implantable cardioverter-defibrillators, it suggests that these markers might be used to identify patients who would benefit from these devices. (Abstract shortened by ProQuest.)

    Central neural mechanisms governing postural cardiovascular mechanisms

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    The results of the vestibular apparatus and cerebellum in orthostatic reflex control are summarized. Mechanisms within the brain which govern circulation reflexes and the consequences of disturbances in their function are also included

    Low-dimensional representations of neural time-series data with applications to peripheral nerve decoding

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    Bioelectronic medicines, implanted devices that influence physiological states by peripheral neuromodulation, have promise as a new way of treating diverse conditions from rheumatism to diabetes. We here explore ways of creating nerve-based feedback for the implanted systems to act in a dynamically adapting closed loop. In a first empirical component, we carried out decoding studies on in vivo recordings of cat and rat bladder afferents. In a low-resolution data-set, we selected informative frequency bands of the neural activity using information theory to then relate to bladder pressure. In a second high-resolution dataset, we analysed the population code for bladder pressure, again using information theory, and proposed an informed decoding approach that promises enhanced robustness and automatic re-calibration by creating a low-dimensional population vector. Coming from a different direction of more general time-series analysis, we embedded a set of peripheral nerve recordings in a space of main firing characteristics by dimensionality reduction in a high-dimensional feature-space and automatically proposed single efficiently implementable estimators for each identified characteristic. For bioelectronic medicines, this feature-based pre-processing method enables an online signal characterisation of low-resolution data where spike sorting is impossible but simple power-measures discard informative structure. Analyses were based on surrogate data from a self-developed and flexibly adaptable computer model that we made publicly available. The wider utility of two feature-based analysis methods developed in this work was demonstrated on a variety of datasets from across science and industry. (1) Our feature-based generation of interpretable low-dimensional embeddings for unknown time-series datasets answers a need for simplifying and harvesting the growing body of sequential data that characterises modern science. (2) We propose an additional, supervised pipeline to tailor feature subsets to collections of classification problems. On a literature standard library of time-series classification tasks, we distilled 22 generically useful estimators and made them easily accessible.Open Acces
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