Artifact correction methods on heart rate variability time series
How to deal with the problem of missing heartbeats in HRV analysis
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Over last years the Heart Rate Variability (HRV) analysis have been rapidly increasing in the academic and industrial world, with applications on devices like smartwatch and smartphones, and in fields like electrocardiography (ECG) telemetry and heart failure predictions. In HRV analysis are used temporal series that contains the distances between successive heartbeats to assess autonomic regulation of the cardiovascular system. These series are obtained from the ECG signal analysis and, in most cases, can be affected by different types of artifacts leading to incorrect interpretations of the ...
Over last years the Heart Rate Variability (HRV) analysis have been rapidly increasing in the academic and industrial world, with applications on devices like smartwatch and smartphones, and in fields like electrocardiography (ECG) telemetry and heart failure predictions. In HRV analysis are used temporal series that contains the distances between successive heartbeats to assess autonomic regulation of the cardiovascular system. These series are obtained from the ECG signal analysis and, in most cases, can be affected by different types of artifacts leading to incorrect interpretations of the analysis of the HRV signals. This study aims to determine the performance of some linear and non-linear correction methods on HRV signals with induced artefacts by quantification of its linear and nonlinear HRV parameters. This book offers a validated methodology to deal with artifacts in HRV signals, that can be applied to a variety of time series in different fields of research. Because ofthis, this book can be used not only by physiologists dedicated to the analysis of cardiovascular signals, but also by people who work with time series and require an optimal pre-processing of their data.
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