Heart rate variability indices for very short-term (30 beat) analysis. Part 1: survey and toolbox

Abstract

Heart rate variability (HRV) analysis over very short (<60 s) periods may be useful for monitoring dynamic changes in autonomic nervous system activity where steady-state conditions are not maintained (e.g. during drug administration, or the start or end of exercise). From the 1980s there has been a wealth of HRV indices produced in the quest for better measures of the change in parasympathetic and sympathetic activity. Many of the indices have been sparingly used and have not been investigated for application to short-term use. This study surveyed published methods of HRV analysis searching for indices that could be applied to very short time HRV analysis. The survey included measures of time domain, frequency domain, respiratory sinus arrhythmia, Poincaré plot, and heart rate characteristics. Indices were tested with short segments of archived data to remove those that produced invalid results, or were mathematically equivalent to, but less well known than other indices. The survey identified a comprehensive list of 115 indices that were subsequently coded and screened. Of these, 70 were unique and produced a finite number with 60 s data, so are included in the Toolbox. These indices require validation against physiological data before they can be applied to short-term HRV analysis of cardiac autonomic nervous system activity.

Statistical considerations for reporting and planning heart rate variability case‐control studies

“…Moreover, p values cannot be used to compare effects between studies that have different sample sizes. Finally, it can be difficult to understand group difference magnitude when an unfamiliar variable is reported-a common occurrence in HRV research as there are over seventy different published metrics (Bravi, Longtin, & Seely, 2011;Smith, Owen, & Reynolds, 2013).…”

Effect of biosignal preprocessing and recording length on clinical decision making for cardiac autonomic neuropathy

“…[13,14] Short time HRV analysis of less than one minute also allows interpretation of dynamic changes associated with heart function such as recovery from exercise and effects of pharmacological intervention. [5] However, these previous studies do not report on preprocessing of the RR tachogram, which plays a role, if not in a 10-second recording then definitely in a 5-minute recording. Our data has shown using the dynamic filtering algorithm proposed by Wessel, which is based on an infinite response filter [10], that the setting of the control variable is an important consideration and needs to be taken into consideration when deciding the length of the RR tachogram to be used for HRV classification tasks.…”

“…One being the number of intervals required for accurate determination of some HRV features and the other the clinical requirement for accurate identification of pathology. [4][5][6] In addition, the type of preprocessing applied to the tachograms should also be routinely considered. [3,7,8] Several methods for automated artifact removal have been proposed.…”

HRVTool - an Open-Source Matlab Toolbox for Analyzing Heart Rate Variability

“…Many diverse HRV indices emerged and are usually classified by nature of processing (time-domain methods, spectral analysis, geometric methods) and the range of application (short and long-term HRV measures), see [1][2][3]. Each of the methods have their specific strength and weaknesses in capturing different characteristics of the interbeat variations.…”