Open a Window into the Autonomic Nervous System Quantifying the amount of autonomic nervous system activity in an individual patient can be extremely important, because it provides a gauge of disease severity in a large number of diseases. Heart rate variability (HRV) calculated from both short-term and longer-term electrocardiograms is an ideal window into such autonomic activity for two reasons: one, heart rate is sensitive to autonomic activity in the entire body, and two, recording electrocardiograms is inexpensive and non-invasive unlike other techniques currently available for autonomic assessment, such as microneurography and metaiodobenzylguanidine (MIBG) scanning. Heart Rate Variability (HRV) Signal Analysis: Clinical Applications provides a comprehensive review of three major aspects of HRV: mechanism, technique, and clinical applications. Learn Techniques for HRV Signal Analysis Edited by an engineer, a cardiologist, and a neurologist, and featuring contributions by widely published international researchers, this interdisciplinary book begins by reviewing the many signal processing techniques developed to extract autonomic activity information embedded in heart-rate records. The classical time and frequency domain measures, baroreceptor sensitivity, and newer non-linear measures of HRV are described with a fair amount of mathematical detail with the biomedical engineer and mathematically oriented physician in mind. The book also covers two recent HRV methods, heart-rate turbulence and phase-rectified signal averaging. Use of HRV in Clinical Care The large clinical section is a must-read for clinicians and engineers wishing to get an insight into how HRV is applied in medicine. Nineteen chapters altogether are devoted to uses of HRV in: Monitoring-for example to predict potential complications in pregnancies, fetal distress, and in neonatal critical care Acute care-for gauging the depth of anesthesia during surgery and predicting change in patient status in the intensive care unit Chronic disorders-for assessing the severity of congestive heart failure, stroke, Parkinson's disease, and depression Bringing together the latest research, this comprehensive reference demonstrates the utility and potential of HRV signal analysis in both the clinic and physiology laboratory.

This monograph is an up-to-date, in-depth and more advanced continuation of its accompanying monograph entitled Brain-Body Interactions: Contemporary Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage using Bayesian Neural Networks and Fuzzy Logic. The current monograph is divided into five sections. The first section synthesises the most current evidence of underlying pathophysiologic mechanisms of brain-body associations in aneurysmal subarachnoid hemorrhage. It also describes pathophysiologic manifestations of central autonomic nervous system dysfunctions in ischemic stroke, intracerebral hemorrhage and aneurysmal subarachnoid hemorrhage. The second section synthesises and critically appraises the methodologic quality of existing studies (including prospective and retrospective cohort studies and randomised controlled trials) that derive clinical predictor tools and clinical predictors used to determine outcome prognosis in patients with aneurysmal subarachnoid hemorrhage. The third section makes use of two aneurysmal subarachnoid hemorrhage databases incorporating advances in the treatment of aneurysmal subarachnoid hemorrhage. Both single prognostic factors and brain-body interactions are explored to make several novel observations which significantly influence clinical outcome in patients with ruptured cerebral aneurysms. In its fourth section, clinical prognostic decision-making tools are created using classification and regression tree analysis. Prognostic subgroups demonstrate the interplay of various underlying pathophysiologic mechanisms which, together, adversely influence long-term neurologic and functional outcomes in those with aneurysmal subarachnoid hemorrhage. Finally, in Section Five, exploratory analyses are conducted using artificial neural networks to further explore the brain-body interface in aneurysmal subarachnoid hemorrhage, with in-depth discussions of the autonomic nervous system and its dysfunction in pathologic states. Using these clinical prognostic models, the clinician can tailor individual-specific treatment efforts to prevent and treat various alterations in the brain-body interface in order to maximise the chances of survival and recovery after aneurysmal subarachnoid haemorrhage.