Heartrate Variability Analysis

Heart Rate Variability Analysis

2013

Research Group on Health Psychology

Tiensestraat 102, box 3726

3000 Leuven (Belgium) [email protected]

+003216/325885

32

Nathalie Claes

FACULTY OF PSYCHOLOGY AND EDUCATIONAL SCIENCES

mailto:[email protected]

CONTENT | 3

Content

Introduction ....................................................................................................................... 5

1 Analyzing Heartratevariability with ARTiiFACT ................................................... 7

1.1 Step 1: Extract IBIs from ECG data ................................................................ 8

1.2 Step 2: Detect and Process artifacts in IBIdata ............................................ 11

1.3 Step 3: Analyse HRV .................................................................................... 13

1.4 Measures of HRV ......................................................................................... 14

2 Interrater reliability ............................................................................................... 15

2.1 Importing your data in SPSS 20.0 ................................................................ 15

2.2 Calculating the interrater reliability with SPSS 20.0 ..................................... 15

3 Outlier analysis of HRV ........................................................................................ 17

3.1 Importing your data in SPSS 20.0 ................................................................ 17

3.2 Visual Inspection of data .............................................................................. 17

3.3 Normalizing variables ................................................................................... 19

3.4 Checking for outliers with SPSS 20.0 and Microsoft Excel 2010. ............... 20

3.5 Outlier exclusion criteria ............................................................................... 21

4 Reporting HRV measures .................................................................................... 21

INTRODUCTION | 5

Introduction

In this paper, the analysis of HRV with ARTiiFACT is discussed. Another topic that is

handled in this paper, is the detection of HRV outliers and what to do with them By no

means this paper means to provide “the” way of conducting the analysis, but merely

presents one way of handling your data.

When conducting research, adequate and correct datahandling is very important,

because it can have serious effects on your results. When using heart rate variability

(HRV) data, one has to pay even more close attention to appropriate data analysis of the

data, since HRV measures are very sensitive to artifacts. Handling artifacts correctly, is

therefore crucial.

One program to analyze HRV, is ARTiiFACT1 (see Kaufmann, Sütterlin, Schulz, &

Vögele, 20112). Video tutorials for this program are freely available on their website.

The programs used to do analysis on the measures (outlieranalysis), are EXCEL 2010

and SPSS20.0. The methods described here however should also be similar in previous

versions of SPSS and EXCEL.

Good luck!

1 ARTiiFACT is a free to use software package developed by Kaufmann, Sütterlin, Schulz and Vögele. More

information can be found on their website: http://www.artiifact.de

2 Kauffman, T., Sütterlin, S., Schulz, S.M., & Vögele, C.(2011). ARTiiFACT: a tool for heart rate artifact

processing and heart rate variability analysis. Behavior research methods, 43(4), 1161-1170. Doi:

10.3758/s13458-011-0107-7

http://www.artiifact.de/

ANALYZING HEARTRATEVARIABILITY WITH ARTIIFACT | 7

1 Analyzing Heartratevariability with ARTiiFACT

After installing ARTiiFACT on your computer, just open it by double-clicking on the icon.

A start-up screen will appear (as shown in Figure 1).

Figure 1. ARTiiFACT start screen.

One important tip I can give you is to define a working directory before starting the

analysis. When you do that, all the files you create will be saved there.

The first option, File conversion, can be used when your data samples are too big and

you want to decrease the sample rate. It usually makes the processing go faster. This is

however not necessary.

The flow of events is as follows: first you complete the step “Extract IBIs from ECG

data”(1) , next you “Detect and process artifacts in IBI data” (2) and lastly, you do the

“Analyse HRV”(3). The Test for normal distribution is again optional and as the name

says, it checks whether the HRV is normally distributed.

Some steps have two options: file by file and batch. For step 1 and 2 it is recommended

to use the file by file step, for the third step, the batch option can be very helpful, cf. infra.

We discuss step 1, 2 and 3 separately.

8 | ANALYZING HEARTRATEVARIABILITY WITH ARTIIFACT

1.1 Step 1: Extract IBIs from ECG data

When clicking on the first step, the screen as shown in Figure 2 appears. You upload a

file by selecting the folder icon. Make sure that there are no variable names in your data

file, otherwise the program will not open your file.

Figure 2. ECG Preparation Module.

When you have selected a file, a window appears asking you to specify the number of

header lines to ignore (should be 0), the samplingrate (Hz) that is dependent on your

recording and downsampling, but typically it is 1000 (or 500 or 250, if you downsize it),

and the channel that you want to process, typically 1.

Figure 3. Specifying options.

When you have specified everything, your ECG data should appear on screen and you

have the option to cut out data that is contaminated. Data that deviates from the whole

set (“peaks”) should be excluded. By clicking the select button in the right bottom of the

screen and clicking the picture of the ECG signal, you can see the specific time of that

data. For example, in the data as shown in Figure 4, the first 19 seconds appear to be

noise, therefore we want to exclude them. We fill in what we wish to cut out and click

apply. The area you want to exclude should color red. If you have more than one area

that contains noise, you just repeat the procedure until every area you want to exclude is

red. Click proceed.


Figure 4. Data of a participant and excluding data.

Now the second phase of the first step can start. A new screen will appear, named

“Automated Peak Detection Module” (See Figure 5). Here you see the ECG signal of your

participant on the right. On the right you have several options to help you detect the R

peaks.

To inspect your data more closely, you can click the magnifying glass with a plus on the

left and click on one of the lines.

The invert ECG data should only happen if you notice that the peaks are inversed: the

negative side of the peak is longer than the positive (up) side. A filter can be used to

better select the peaks, either low or high pass. Most of the time, the high pass is used,

and frequency is defined as 0.1 Hz, but this value can differ. In the example, it was 10.

Also the peak detection method can be selected. If there were no trouble with your data

collection, the global threshold option will do. To define the threshold, you can click the

graph on the bottom and select the global peak level there. It can differ between

participants.

Also, the distribution of the R-R intervals are shown in a small graph on the left.

When your filter settings are right, all the peaks (and some noise) will be marked in blue.

When ready, click proceed.


Figure 5. Automated Peak Detection Module.

A new screen will appear, with the possibility to manually select or deselect peaks.

Accurate peak detection is important!

For example, in Figure 6 is an area with a lot of noise, and you cannot know which is a

peak and which is not. Therefore, you click the select button, select the peaks you want

to mark as non-peaks (hold down alt when selecting multiple peaks). When you have

selected all, you click the “mark as non-peak” button. When the program does not detect

a peak it should detect, you can select it and push the mark as peak button. When ready,

proceed.

Figure 6. Manual Peak Detection Module.


A screen will appear (see Figure 7) showing you the distribution. You can give a name to

the outputfile and choose to save it in excel, txt and/or pdf. Click save and quit to continue

to the next step.

Figure 7. Store IBI module.

1.2 Step 2: Detect and Process artifacts in IBIdata

Now you have finished the first step, you can detect and process the artifacts in the

IBIdata. Select the file by file option.

Select the file that you created in the previous step. Anew, you have the option to cut

away data, as you can see in Figure 8, some points around 100 ms and 300 ms could be

deleted because they deviate too much from the rest from the sample. If you are

insecure, whether they are artifacts or not, just proceed. In the next step, the program will

tell you which it considers artifacts.

Figure 8. IBI Data Preparation Module.


Next, the program shows you if there are artifacts in your dataset and indicates them in

red. It is best to check the data yourself, because sometimes the program doesn‟t detect

artifacts it should detect, or it falsely indicates something as an artifact that is not (as

shown in Figure 9).

An artifact is usually very visible: it deviates from the rest of your data, either upward or

downward. Also, when two datapoints right after eachother are on equal height, it can be

considered as an artifact: how big is the chance that two consecutive intervals are exactly

the same size?

To select a datapoint, first click the select option and next click the datapoint(s). Hold

down alt to select multiple options. To mark it as an artifact or as valid data, click the

respective button. Proceed.

Figure 9. Detecting Artifacts.

In the last step of step 2 (see Figure 10), you can choose a processing method of

handling the artifacts. The method most used is the cubic spine interpolation. Click the

refresh button next to the select method to run it. When it is done, the graphs should

appear. Give your file a name and save it as a txt-file, an excel-file or/and an

RSAtoolboxexport. Save and quit. Repeat step 1 and 2 for every participant.


Figure 10. Interpolating data.

1.3 Step 3: Analyse HRV

When you completed step 1 and 2 for every participant, first create a new map where you

put the files created in step 2 for every participant. Specify the file as your working

directory.

Next, click the batch option. Specify the map you just made and select in what format

your outputfile should be saved. It is advisable to select only excel, since we will import

the ratings in SPPS 20.0 and pdf-files will slow down the process. Click the refresh button

on the bottom to start processing. The program shows you its progress and tells you

when it is done. Your results can be found in the map you specified, for each participant

separately as well as a summary file. In the summary file, participants are identified by

the file name.

Figure 11. Analysis of HRV.


1.4 Measures of HRV

The HRV measures that you will get out of the program are divided in a Time and a

Frequency Domain. In table 1 you can find a definition of every measure that you will find

in the output.

Table 1. Overview of HRV measures

Some general remarks must be made about the different measures, however. The

RMSSD for example is widely used, because it is a very precise measure, but it is also

very sensitive to the experimental conditions.

The NN50 is a rarely used measure, instead the Pnn50 is used (see TaskForce, 1996;

and Ellen & Chambers, 2007).

The LF is a depiction of both sympathetic and parasympathetic activity and is not easy to

interpret. Therefore, it is not often used as a measure, but it is mentioned in the

descriptive table. When you normalize the LF, it is closest as you can get to a measure of

sympathetic activation. The HF on the other hand is a breathing-related measure that is

very precise. However, it is also a very sensitive measure. When normalized, it is very

dependent on the LF.

Measure Definition

Time Domain Mean RR

mean of interbeat (R-R) intervals

Median RR

median of interbeat (R-R) intervals

Mean HR

mean of heartrate

SDNN

standard deviation of interbeat (R-R) intervals

RMSSD

Root mean square of successive interbeat (R-R) intervals

NN50

number of interbeat (R-R) intervals that differ more than 50 ms

pNN50

percentage interbeat (R-R) intervals that differ more than 50 ms

Frequency Domain VLF (%)

percentage of very-low-frequency component

LF (%)

percentage low-frequency component

HF (%)

percentage high-frequency component

LF/HF

ratio of low and high frequency

VLF (ms2)

very-low frequency component in ms

2

LF (ms2)

low frequency component in ms

2

HF (ms2)

high frequency component in ms

2

LF (n.u.)

normalized units of low frequency component

HF (n.u.)

normalized units of high frequency component

INTERRATER RELIABILITY | 15

2 Interrater reliability

Since artifacts can influence the HRV measures in various ways that contaminate your

results, it is best to do the HRV analysis with two independent people.

Next, rename the variables so that you can match them, e.g.: RMSSD1 for one rater,

RMSSD2 for the other rater and add the files together in one excel-file.

2.1 Importing your data in SPSS 20.0

When all your measures are in one file, you can open the file in SPSS 20.0. To do so,

you go to File → Open data and you will need to select the correct file, as shown in the

figure below.

Figure 12. Opening a datafile in SPSS.

2.2 Calculating the interrater reliability with SPSS 20.0

To calculate the interrater reliability of the two raters, you go to Analyze → Scale →

Reliability analysis. Do not forget to select the options you want in the statistics pane.

Another way is to use syntax. Use the RELIABILITY command and specify which

variables need to be compared and the model and scale you want to use. In this

example, the interclass correlation coefficient (ICC) is calculated.

An example:

16 | INTERRATER RELIABILITY

DATASET ACTIVATE DataSet1.

RELIABILITY

/VARIABLES= RMSSD1 RMSSD2

/SCALE('ALL VARIABLES') ALL

/MODEL=ALPHA

/STATISTICS=CORR ANOVA

/ICC=MODEL(MIXED) TYPE(CONSISTENCY) CIN=95 TESTVAL=0.

The output will look like this:

In the example above, the intraclass correlation is .85, and the F-test is significant. You

also get a chronbach‟s alpha, but this is a measure of internal consistency. For interrater

variability, it is better to report the ICC. The ICC value lies in between 0 and 1. The lower

the ICC, the more variation in the method of judgment.

OUTLIER ANALYSIS OF HRV | 17

3 Outlier analysis of HRV

Before using your data to test your hypothesis, it is best to check for outliers in Heart

Rate Variability. There is no perfect way to check for HRV outliers, but it is important to be

transparent about the method you used. The method described here is just one of many

methods, and one can argue to use another method.

Here, we first import our HRV data in SPSS 20.0, and subsequently delete data from

participants that will be excluded from the experiment. Next, we inspect the data visually,

by means of boxplots, histrograms and QQplots. Also, normality of distribution is checked

and if need be, the measures which are skewed are normalized. When there are outliers

in your file, decisions need to be made on the in- or exclusion of the specific outlier.

3.1 Importing your data in SPSS 20.0

Import your datafile with all the participants in SPSS 20.0 as descriped in 2.1. Next,

recode the first variable so that you can identify your participants the same way as in

your experiment dataset, e.g. “ID”. Sort it ascending.

If you have to exclude participants (based on exclusion criteria, technical failures, etc.),

do that before you start the inspection of your data.

3.2 Visual Inspection of data

A first step in detecting outliers in your HRV data, is to visually inspect your data. To

inspect your data, go to Analyze → Descriptives → Explore. A screen will appear (see

Figure 13). Put every variable you want to inspect in the dependent list. To know which

cases are outliers, best put your identifier in the label cases by option. On the right, you

have the possibility to specify statistics, plots and options. In the statistics pane, you can

select that you want to see outliers. In the plots option, you can specify that you want

normality tests.

18 | OUTLIER ANALYSIS OF HRV

Figure 13. Exploring your data.

Another option is use the EXAMINE variables command in your syntax. In ID you put the

variable you want to label your cases with. In the PLOT command you indicate the

graphs you want in your output, the same is true for the statistics command. An example:

EXAMINE VARIABLES=MEANRR

/ID=PPN

/PLOT BOXPLOT STEMLEAF HISTOGRAM NPPLOT

/COMPARE GROUPS

/STATISTICS DESCRIPTIVES EXTREME

/CINTERVAL 95

/MISSING LISTWISE

/NOTOTAL.

Regardless of the method you use, you get a table with descriptives, extreme values, and

a table with tests of Normality. Often the Kolmogorov-Smirnov is used, and indeed, it can

give you an indication of normality. Another way of checking if the distribution for your

variable is normal, you can check the Normal Q-Q Plot. As you can see in Figure 14, the

distribution of the variable there is quite normal, but you have an outlier on top.

Figure 14. Normal Q-Q Plot of variable MEANRR.

OUTLIER ANALYSIS OF HRV | 19

Another option to check a normal distribution is inspect the histograms and draw a normal

distribution line in your graph.

To confirm if you have an outlier, you can next inspect the boxplot of the respective

variable. And indeed, as shown in Figure 15, meanRR has one outlier, more specifically

participant 117. Whether you should exclude the participant or not, is dependent on a

number of things, cf. infra.

Figure 15. Boxplot of variable MEANRR.

3.3 Normalizing variables

Some HRV measures are sensitive and are often skewed, for example: pNN50, HFabs,

and RMSSD. Before you do any calculations with them, it is best to normalize the

variables that are skewed in your specific dataset.

To normalize the variables, a log transformation is often used, like for example the

natural logarithmic transformation (ln) or the log10 transformation.

To calculate the log transformation, go to Transform → Compute Variable. Give the

new variable a name and use the LN(var) or Log10(var) option, e.g. COMPUTE

LogRMSSD=LN(RMSSD).

After you did that for all the variables that needed normalizing, check the normality

distributions and outliers again for these variables.

20 | OUTLIER ANALYSIS OF HRV

3.4 Checking for outliers with SPSS 20.0 and Microsoft Excel

2010.

To check for outliers, you first calculate the mean and standard deviation for every HRV

variable. Go to Analyze→ Descriptives → Descriptives. Or use a syntax, e.g:

DESCRIPTIVES VARIABLES=MEANRR MEDIANRR MEANHR SDNN RMSSD NN50 Pnn50 VLF

LF HF LFHF LFn.u HFn.u VLFabs LFabs HFabs LogRMSSD LogPNN50 LogNN50 LogLFHF

LogLFabs LogVLFabs LogHFabs

/STATISTICS=MEAN STDDEV.

Next, decide on a criterion for exclusion of outliers. Common is the “mean ± 2/2.5/3 SD”-

criterion. The conservativeness of your criterion is an unavoidably subjective choice. One

remark here is that you should be consistent in the criterion you use for all your data, or

have good, justifiable reasons to do otherwise. In the example, 3 SD deviation from the

mean is used as an criterion. To calculate the range of your data, click on the table with

descriptives, and click export and export it to Excel. Open your excel-file and select a

new column next to the table. In the first column you put in a formula to calculate the

Mean – 3 SD, in the second column Mean + 3 SD option. For example, when for the

variable MeanRR, the mean is in cell D3 and the SD is in cell E3, the formula is “=D3 –

(3*E3) for the lower bound. Drag to extrapolate the formula for all the variables.

Subsequently, you can create new variables that indicate if a case is an outlier (1) when it

is not within the range of the mean SD or not (0). To do so, you recode the variable in a

new variable as follows:

RECODE MeanRR(378.03 thru 1227.4 = 0) (ELSE=1) INTO OutlierMeanRR.

EXECUTE.

To see if you have outliers and which cases are variables, you go to the Analyze →

Descriptives → Explore pane. Select the option to see the highest and lowest values.

Or use a syntax:

EXAMINE VARIABLES=OutlierMeanRR OutlierMedianRR OutlierMeanHR OutlierSDNN

/ID=PPN

/PLOT NONE

/STATISTICS DESCRIPTIVES EXTREME

/CINTERVAL 95

/MISSING LISTWISE

/NOTOTAL.

REPORTING HRV MEASURES | 21

It is possible that SPSS warns you he cannot show you a boxplot, since the variable is “constant”. This means that for that variable you have no outliers. For the other variables, check the table with „highest‟ values. Every case with a 1 should be there. These are outliers for that variable.

3.5 Outlier exclusion criteria

Now you know which cases are outliers for specific variables, it is time to decide what to

do with the outliers. The criteria/rules described here is just one method, which is not

perfect. The most important aspects of making decisions are (a) being consistent in what

you do, and (b) discuss your results with a colleague to have good interrater reliability of

analysis decisions.

When detecting outliers, you first have the criterion of the mean +- of 3 SD. Look at each

outlier separately. When you have an outlier in meanRR, first check the original files for

artifacts. If it is an artifact, redo the analysis and use the new information. If it is however

not an artifact, exclude the whole case.

If there is an outlier in the Time domain (cf. supra), we opt to exclude the whole case.

If there is an outlier in the Frequency domain (cf. supra), like for example in LFabs or

HFabs, we would opt to exclude only that specific variable, but not the Pnn50, because

that is a very robust measure. When in doubt, you can always delete the complete

variable or consult colleagues.

4 Reporting HRV measures

When collecting HRV data and reporting it, it is important to report (a) the method and

program of collection and analysis, (b) the handling of artifacts, (c) the HRV measures

used, (d) test of normality, (e) which variables were normalized, (f) if there were outliers

and what happened to them.

Also, a descriptive table should be included in your report, with the mean, standard

deviation, a range or confidence intervals for the HRV measures, both the original as the

normalized values. Make this descriptive table after you did the exclusions, on the

included sample.

RESEARCH GROUP ON HEALTH PSYCHOLOGY Tiensestraat 102 box 3726

3000 LEUVEN, BELGIË tel. + 32 16 32 58 85

[email protected] http://ppw.kuleuven.be/english/ogp

http://ppw.kuleuven.be/english/ogp

Heartrate Variability Analysis

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