Heart rate variability and the dawn of complex physiological signal analysis: methodological and clinical perspectives

Abstract

Spontaneous beat-to-beat variations of heart rate (HR) have intrigued scientists and casual observers for centuries; however, it was not until the 1970s that investigators began to apply engineering tools to the analysis of these variations, fostering the field we now know as heart rate variability or HRV . Since then, the field has exploded to not only include a wide variety of traditional linear time and frequency domain applications for the HR signal, but also more complex linear models that include additional physiological parameters such as respiration, arterial blood pressure, central venous pressure and autonomic nerve signals. Most recently, the field has branched out to address the nonlinear components of many physiological processes, the complexity of the systems being studied and the important issue of specificity for when these tools are applied to individuals. When the impact of all these developments are combined, it seems likely that the field of HRV will soon begin to realize its potential as an important component of the toolbox used for diagnosis and therapy of patients in the clinic. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Reliability of Symbolic Analysis of Heart Rate Variability and Its Changes During Sympathetic Stimulation in Elite Modern Pentathlon Athletes: A Pilot Study

“…In recent years, measures of the non-linear dynamics of HRV have provided new opportunities to monitor cardiac autonomic regulation ( Voss et al, 2009 ; Sassi et al, 2015 ; Henriques et al, 2020 ). It is known that cardiovascular control involves non-linear interactions between physiological systems, with complex dynamics ( Schiecke et al, 2018 ; Saul and Valenza, 2021 ). Considering that many biomarkers are non-stationary signals ( Goldberger, 1991 ; Faust et al, 2004 ) and vary in a complex and non-linear way ( Voss et al, 2009 ), some authors highlight that non-linear HRV indices are more suitable than linear for the evaluation of individual changes in test–retest studies ( Maestri et al, 2007 ) and underline the need to perform non-linear analysis to provide holistic information on HRV ( Acharya et al, 2006 ; Huikuri et al, 2009 ; Voss et al, 2009 ; Hoshi et al, 2013 ; Sassi et al, 2015 ).…”

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

“…The beat rhythm of the heart is transmitted to the pulse with the blood, and both ECG and pulse period sequences can effectively reflect heartbeat rhythm changes (Mitchell and Schwarzwald, 2021). Heart rate variability (HRV) is calculated from ECG, which reflects the rate of the heartbeat and is used to assess the autonomic nervous system of the heart (Ishaque et al, 2021); thus, HRV can be used for the diagnosis of cardiovascular diseases (Saul and Valenza, 2021). The PRV is extracted from the ABP signal, which reflects the subtle changes in the vascular pulse cycle (Jan et al, 2019).…”

Biomedical Signal Processing: The Cornerstone of Artificial Intelligence in Healthcare Wearables

“…This may lead to inconsistencies due to the variety of experimental set-ups and methodological approaches used in validation studies. Let us illustrate this autonomic non-specificity with respect to vagal driven heartbeat dynamics in resting state: similar variations in HRV series may be observed during simple postural changes in healthy subjects as well as during unstructured activity in congestive heart failure [20]. In fact, these healthy and pathological states are associated with a significant sympathetic-driven dynamics on cardiac control.…”