Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system.

By J. Sztajzel. Swiss medical weekly, 2004

827 citations recorded by the source corpus.

The autonomic nervous system (ANS) plays an important role not only in physiological situations, but also in various pathological settings such as diabetic neuropathy, myocardial infarction (MI) and congestive heart failure (CHF). Autonomic imbalance associating increased sympathetic activity and reduced vagal tone has been been strongly implicated in the pathophysiology of arrhythmogenesis and sudden cardiac death. Among the different available noninvasive techniques for assessing the autonomic status heart rate variability (HRV) has emerged as a simple, noninvasive method to evaluate the sympathovagal balance at the sinoatrial level. It has been used in a variety of clinical situations including diabetic neuropathy, MI, sudden death and CHF. The standard measurements intervening in the analysis of HRV comprise time domain indices, geometric methods and components of the frequency domain. Measurements of HRV are generally performed on the basis of 24 hour Holter recordings (long-term recordings) or on shorter periods ranging from 0.5 to 5 minutes (short-term recordings). The use of long or short-term recordings depends on the type of study that has to be realised. Established clinical data based on numerous studies published during the last decade consider decreased global HRV as a strong predictor of increased all-cause cardiac and/or arrhythmic mortality, particularly in patients at risk after MI or with CHF. This article reviews the mechanism, the parameters and the use of HRV as a marker reflecting the activity of the sympathetic and vagal components of the ANS on the sinus node, and as a clinical tool for screening and identifying patients particularly at risk for cardiac mortality.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine

View Paper: https://www.semanticscholar.org/paper/f95c7a85e113349f8e2dc0cdb563b6f35fc0d0b2

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Citations in the corpus

Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2016, methodology): “The most common parameters are the mean value and the Standard deviation of RR intervals (SDRR), the Root Mean Square (RMSSD) and the standard deviation (SDSD) of RR intervals’ successive differences, the standard deviation of the averages of NN intervals in all 5-min segments of the entire recordin”
Heart Rate Variability (HRV) Analysis: A Methodology for Organizational Neuroscience (2019, methodology): “Among them, the analysis of heart rate variability has emerged as one of the most rapid and noninvasive methods used to obtain reliable and reproducible information on the autonomic modulation of heart rate (Parati, Mancia, Di Rienzo, & Castiglioni, 2006; Sztajzel, 2004).”
Behavioural and physiological assessment of positive and negative emotion in sheep (2009, background): “In experimental situations, short-term reactions are commonly assessed in measurement periods of approximately 5 min, as recommended for example for heart rate (Kim et al. 2004; Sztajzel 2004).”
Emotion recognition using wireless signals (2016, methodology): “RMSSD is typically used as a measure of the parasympathetic nervous activity that controls the heart [57].”
Why trust an algorithm? Performance, cognition, and neurophysiology (2018, background): “Heart rate variability is defined as the variation in the time intervals between adjacent heartbeats (Sztajzel, 2004).”