HRVTool – an Open-Source Matlab Toolbox forAnalyzing Heart Rate Variability

Marcus Vollmer1,2

1 Institute of Bioinformatics, University Medicine Greifswald, Germany2 German Centre for Cardiovascular Research (DZHK), Greifswald, Germany

Abstract

Motivation: Many software tools for ECG processingare commercial. New innovative and alternative featuresfor heart rate variability analysis (HRV) and improvedmethods in ECG preprocessing cannot be incorporated.Moreover, software manuals are lacking of clarity and of-ten conceal the exact calculation methods that makes clin-ical interpretation difficult, and reproducibility is reduced.

Software description: HRVTool provides an open-source and intuitive user-friendly environment for theHRV analysis in Matlab. The software is availableat http://marcusvollmer.github.io/HRV and supports theprocessing of ECG, pulsatile waveforms and RR inter-vals from various sources (mat and text files containingraw data, Polar, PhysioNet, Hexoskin, BIOPAC, Euro-pean Data Format, ISHNE Holter Standard Format, andMachine-Independent Beat files). An integrated heart beatdetector locates R peaks or pulse waves. Visual inspection,and manual adjustments of beat locations are possible andthe corresponding annotation file can be saved in a stan-dard Matlab format or as a delimited text file. HRV statis-tics are computed in a sliding window to evaluate the alter-ation over time. HRV metrics can be exported. An anima-tion of intervals supports pattern identification. Moreoverthe Matlab class (HRV.m) includes functions for windowedHRV computation that can be used for batch processing.

1. Introduction

Heart Rate Variability (HRV) analyzes the physiologicalphenomenon of heart beat variation over time and is usedto determine autonomic activity of the heart [1]. Disordersin the regular heart rate as a result of disturbances in theelectrical system of the heart are called arrhythmia. Expertcardiologists can identify such a physiological variationof the heart rate by analyzing the ECG leads (electrocar-diogram) and thereby diagnose different cardiac disorders.In contrast to arrhythmia screening, HRV as a measure ofneurovegetative activity is used in remedial medicine forprognostic purposes, performance objectification and risk

stratification for cardiovascular events. HRV biofeedbackmethods are established and heart rate analysis and contin-uous assessment of HRV as objective features are widelyused beyond the medical environment, e.g. for assessingthe fitness status, physical and mental stress level.

Many diverse HRV indices emerged and are usuallyclassified by nature of processing (time-domain methods,spectral analysis, geometric methods) and the range of ap-plication (short and long-term HRV measures), see [1–3].Each of the methods have their specific strength and weak-nesses in capturing different characteristics of the inter-beat variations.

Important for valid HRV results is the quality of theECG and the robust extraction of inter-beat intervals (IBIsor RR intervals) because many indices are not robustly de-fined in such a way, that wrong RR intervals do not distortthe overall analysis – although there are some robust mea-sures such as rrHRV [4]. In general also pulsatile measure-ments from a fingertip photoplethysmogram or throughballistocardiography are suitable to extract IBIs.

Popular free HRV software libraries includes: Ku-bios [5], PhysioNet Cardiovascular Signal Toolbox [6, 7],RHRV [8, 9], ARTiiFACT [10], HRVAS [11], SinusCor[12], and hrvanalysis 1.

These software libraries usually consists of data im-port tools with automated algorithms for IBI extraction.Modules for data screening, manual adjustments, and datacleaning are integrated. HRV indices are computed, re-sults are shown, and the exports made available. The toolsdiffer in providing open access to the made calculations,the usage of HRV indices, the filtering of IBIs for robustmeasures, accepted data formats, the style of data repre-sentation, and user-friendly handling [6].

Usually, new innovative and alternative features forheart rate variability analysis and improved methods inECG preprocessing cannot be incorporated. We also seedeficits that software manuals are lacking of clarity and of-ten conceal the exact calculation methods that makes clin-ical interpretation difficult, and reproducibility is reduced.

1https://github.com/Aura-healthcare/hrvanalysis

In this paper we present an open-source and com-prehensive Matlab class with transparent code for HRVanalysis and an open-source intuitive user-interface forthe HRV analysis in Matlab (HRVTool v1.04 updatedSep 1, 2019). The toolbox and its supporting func-tions for data import and processing, except of BIOPACimport functions by Jimmy Shen, are open-source li-censed under MIT license and made freely available athttps://github.com/MarcusVollmer/HRV, the project pagehttp://marcusvollmer.github.io/HRV/, and Matlab Central.

2. Computational methodsData import and heart beat detection Supported filetypes are listed in Table 1 and includes several widely usedformats [13] and particular manufacturer formats.

Table 1. Table of supported filesDescription

HRM Polar filesMAT Matlab files containing waveforms or

RR intervals (in ms)TXT text files containing waveforms or

RR intervals (in ms)ECG PhysioNet files

(PhysioNet wfdb toolbox required)WAV Hexoskin filesEDF European Data FormatACQ BIOPAC data (Source code of Jimmy Shen)ISHNE Holter Standard Format

(ECG and annotation data)MIBMIBF

Machine Independent Beat file(GE Marquette holter format)

For EDF, MIB, ISHNE, and ACQ particular importfunctions exist in the GitHub repository, that are com-mented and can be used for batch processing. In case ofEDF files, all signals can be imported as a Matlab structureor particular signals can be selected from a drop-down list.HRVTool allows the import of existing annotation files ofheart beat locations or runs a robust automated detectionalgorithm [14] for the identification. Recommended pa-rameters can be chosen from a drop-down list to allow thedetection in human or animal (rat) ECGs or in pulsatilesignals with fast (less accurate) or normal detection speed.Manual settings can be added to qrs settings.mat.

HRV.m – a Matlab class for HRV analysis HRV.m con-stitutes an extendable and transparent Matlab class with di-verse functions for continuous HRV assessment. All avail-able methods listed in Table 2 uses matrix operations asfar as possible to obtain local HRV measures from long se-quences of RR intervals. Missing values are supported andno additional Matlab toolboxes are required.

Batch processing The sample batch function (availableon GitHub, HRV sample batchjob.m) shows an ex-

Table 2. Table of available methods in HRV.mName DescriptionSDSD Compute standard deviation of succes-

sive differencesSDNN Compute standard deviation of NN inter-

valsRMSSD Compute root mean square of successive

differencespNNx Probability of intervals greater xms or

smaller −xmspNN50 Probability of intervals greater 50ms or

smaller −50mstriangular val Compute Triangular Index and TINNTRI Compute Triangular index from the inter-

val histogramTINN Compute TINN, performing Triangular

InterpolationDFA Perform Detrended Fluctuation AnalysisCD Compute the Correlation DimensionApEn Approximate Entropyfft val fun Spectral analysis of an IBI sequence

(LF,HF,ratio)fft val Continuous spectral analysis in a win-

dowed approach (LF,HF,ratio)returnmap val Results of the Poincare plot

(SD1,SD2,ratio)HR Compute the average heart raterrx Compute relative RR intervalsrrHRV Compute HRV based on relative RR in-

tervalsRRfilter Remove artifacts from RR sequences us-

ing rrxpattern Recognition of patterns and regularities

in data

emplary workflow to process many recordings in a loop.It shows how HRV measures are calculated from the HRVclass and stores results in an Excel spreadsheet.

3. Software descriptionTo analyze HRV and for a better understanding of the

cardiovascular activity, an open-source graphical user in-terface (GUI) has been created with Matlab 2019a. TheGUI of HRVTool is hard-coded and does not make useMatlab building apps (GUIDE/App-Designer). All com-ponents can be modified without asking. The softwarecomes with three different color schemes: dark (de-fault), light, and user-specific. Own colors can be set inclr user.mat.

The Interface The HRVTool GUI is a one-page screenand starts initially with sample data, such as shown in Fig-ure 1. The menu bar (from left to right) consists of iconsenabling the ‘Data import’, ‘Saving of settings’ (appear-

ECG_I.wav

3540 3545 3550 35550

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ECG_I.wavSample ECG

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3540:3555P2

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Add

Save annotations

HRV measures

global local footprint3540:3555

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IQR

shift

rrHRV

Mean RR | HR

SDNN

RMSSD

pNN50

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SD1 | SD2

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LF | HF

LF/HF ratio

3.84

3.59

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760 | 79

125.5

30.2

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41.7

625

21.3 | 175.9

0.12

NaN | NaN

NaN

8.83

4.41

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782 | 77

85.9

51.2

47.1

6.0

16

34.8 | 112.4

0.31

66.8 | 33.2

2.01

2.40

2.16

(+1.13,+1.03)

807 | 74

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18.5

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Local Return Map

1

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0.15 0.4Frequency (Hz)

Spectrum of RR Tachogram

LF/HF ratio = 1.261

LFnu55.76%

HFnu44.24%

global<>local

00:15 00:45 01:15 01:45 02:15 02:45 03:15

75

105

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Segmentwise HRV analysis

P2P1 HF

rrMedianrrIQRRMSSDSD1/SD2

0.4 0.6 0.8 1 1.2

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Global Return map

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60 0.75 visibility on/offLFHF TINN

HRVTool 1.04 | marcus.vollmer@uni-greifswald.de

Figure 1. The graphical user interface of HRVTool with light color scheme is divided into five segments: (top) RR intervalseries or waveform with annotation editing, (middle-left) HRV results, (middle-center) return map of selected RR intervalperiod with option for real time animation of interval dynamics, (middle-right) RR interval tachogram and spectral density,(bottom) time-course of heart rate and HRV parameters and return map of entire IBIs with option to mark outliers and toshow the related period of RR intervals.

ance of the GUI), ‘Printing’, ‘Matlab property inspector’,‘Copy to clipboard’ (of results in a Matlab matrix format),adjustment of ‘Font size’, selection of the ‘Color scheme’,and to set the ‘Record title’. Furthermore, it is possible tosave the analysis as a pdf figure, Matlab figure, png, matfile, or a csv file containing the HRV results as given in thescreen (‘files/save as’).

The top module of the HRVTool shows the ECG, pulse,or IBIs in a narrowed window. Buttons can be used fornavigation or the period of interest can be directly enteredin the rightmost edit field above the plot, either as SS:SSor HH:MM:SS..HH:MM:SS. The selected period of datacan be named by entering a name in the first edit fieldabove the plot. The name appears in the HRV measurestable (middle-left) and in the segment-wise HRV plot atthe bottom. The y-axis can be scaled automatically using

the leftmost button in the upper section. By default, RR In-tervals or IBIs are filtered from noise, artifacts and excep-tional beats using relative RR intervals [15]. The intensitycan be changed (see ‘Filter’ manipulation right to the timeseries plot). Non-used IBIs are displayed as ‘NaN’ and notused in HRV calculations. Entering NaN in the filter editfield disables the filtering of RR intervals.

Current IBIs are visualized in a local return map in themiddle of the screen. A button will change the displayfrom relative to absolute pairs of IBIs. There are buttonsto change the display according to the needs, and the foot-print button will temporary save the results of the selectedperiod to allow the direct comparison with other periods.The footprint is illustrated as gray dots and adds a separatecolumn in the left handed table with HRV measures thatdenotes the characteristics of the footprint period. Periodic

rhythms in the IBIs (e.g. from respiratory sinus arrhyth-mia) are usually visible here and complex rhythm patternscan be animated by pressing the ‘play button’ in the rightupper corner of this module (with speed edit field). Rightto the center the RR tachogram and its spectral density isplotted. The spectrum uses the fast Fourier transform onnon-filtered, spline-interpolated RR interval series.

Time windowed analysis The widescreen plot on thebottom is showing HRV measures computed from a fixednumber of beats in a windowed approach. A fixed numberas given in the first edit field stabilizes the level of impre-cision of HRV measures. The parameter in the second editfield is the degree of overlapping windows (0.25 means25% overlapping, 0 means distinct periods). At each timepoint only past RR intervals are used and the higher im-preciseness at the beginning is highlighted through a redshaded ground in the plot. The ‘visibility’ icon is here tohide and show certain measures. The ‘pin’ icon enablesthe selection of named periods in the experiment.

Manual correction of beat annotations HRVTool en-ables the editing of annotation files. Several buttons on theright of the upper plot allows the manual removal and in-clusion of beat locations. Beats which should not be usedin the HRV analysis can set to be ‘ignored’. All or justthe local beats can be aligned to the close maximum orminimum peak. Final annotation files can be saved in afree format (as mat, txt or csv files). It’s recommended tovisually checking outliers in pairs of successive RR inter-vals using the global return map in the bottom right plot bydouble clicking the outlier after pressing the ‘pin’ icon.

4. SummaryHRVTool constitutes an intuitive easy-to-use graphical

user interface with transparent methods and convenientways for modification or to link and edit certain methodsin the HRV analysis pipeline. Ignored beats, labels of pe-riods and GUI-related properties can be saved at any timeto allow the continuation of the manual HRV analysis. Forthis purpose a settings file will be generated in the samedirectory as the input file. Moreover, batch processing ispossible with the HRV class, supported by individual func-tions for data import and heart beat detection.

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Address for correspondence:

Marcus Vollmer / marcus.vollmer@uni-greifswald.deInstitute of Bioinformatics / University Medicine GreifswaldFelix-Hausdorff-Str. 8 / 17475 Greifswald / Germany