ECG Time Series Variability Analysis – The nexus between engineering and biological science including medicine is a rich area of scientific endeavor in terms of understanding physiological and pathophysiological processes. Advances in computer technology and medical instrumentation have led to opportunities to improve data acquisition, signal processing, and analysis.
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Much has been written about heart rate variability (HRV) with more to come as engineers, computer scientists, physicists, mathematicians, and clinicians unravel its mysteries. Cardiac rhythms are complex and reflect the physiological or pathophysiological neural control and feedback mechanisms over time. HRV is a statistical measure of the changes in heart rate or the interbeat intervals over time, and may be obtained from recordings as short as 10 seconds or several days in length. However, clinical diagnostic/therapeutic utility and the usefulness of HRV are still being debated in the literature and are limited in application. ECG Time Series Variability Analysis
Automated stratification of cardiovascular diseases by HRV of patients at rest provides an additional clinical tool with high accuracy and reliability that does not require patient participation and can be corrected for age and gender. Recordings of HRV can be obtained from a person either in a supine or sitting position or while on a head-up tilt device. In all cases, short recordings with no active participation required provide a highly useful tool in clinical settings. ECG Time Series Variability Analysis
It is not the intention of this book to provide detailed descriptions of the methods associated with preprocessing of the heart rate time series and related analytical methods, but rather to provide an overview of the field from an engineering and medical perspective, describing the current state of the art applied to understanding pathophysiological processes in disease and disease progression. This book ECG Time Series Variability Analysis aims to describe the complex time series of the heart rate and how to interpret results for a better understanding of information obtained by the diverse methods applied in HRV analysis and its role in health and disease.
Developing engineering solutions allows clinical science and medical diagnostics to move forward and improve patient care by identifying often asymptomatic disease progression early. HRV analysis has progressed from applying simple time-domain- and frequency-domain–based methodology to applying more complex algorithms toward discovering better and more meaningful descriptors of the inherent variability of the heart rate over time and its meaning. Some of these algorithms are discussed in the following chapters. ECG Time Series Variability Analysis
Biosignal processing and analysis remains an exciting field as it continues to expand, providing novel measures relevant to diverse fields such as biomedical engineering, computing, physics, mathematics, and medicine, to name a few. Linear methods of time and frequency analysis have given way to nonlinear analysis such as fractal geometry and entropy-based methods. These include multiscale entropy, where the scaled measures are thought to provide additional useful information. In many cases, these have led to the identification of subtle changes in the HRV associated with pathological processes even though the physiological meaning is yet to be found.
For any analysis to provide meaningful results, the method applied has to match the data to be analyzed. Hence the basis for HRV can be placed at the bidirectional regulatory mechanisms of the autonomic nervous system (ANS). Parasympathetic input acts as a heart rate brake, slowing heart rate, whereas the sympathetic component or withdrawing of the parasympathetic input of the ANS increases heart rate. The ANS is further regulated by the brainstem, subcortical, and cortical areas, with which it essentially has a closed loop. Anxiety, depression, and schizophrenia are examples of cortical pathologies that affect HRV. Parkinson’s disease and brainstem pathology as well as peripheral pathophysiology such as associated with diabetes further change the heart rate pattern. These changes in HRV or the specific HRV results are not specific for a particular pathology but hint at a change away from body homeostasis that can be used for supporting a clinical diagnosis and interpretation with involvement of the ANS as well as an indicator of treatment effectiveness. ECG Time Series Variability Analysis
The first chapter of the book provides historical context and an introduction to basic biosignals analysis, including some recent advances in HRV algorithm development. It is intended mainly for physicians to familiarize themselves with this area of inquiry. The remaining chapters provide biological and clinical examples of how various HRV measures are applied in biology and specifically in autonomic neuroscience, exercise physiology, cardiac function, renal disease, mental health, fetal health, and pediatrics. The key difference from contemporary HRV-related books is that the current book provides additional insights into the pathophysiological link between physiologically understandable mathematical indices of HRV and ANS function in health and disease.
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