A New Tool for the Analysis of Heart Rate Variability of Long

Duration Records

Ricardo Chorão

, Joana Sousa

, Tiago Araújo

1,2

and Hugo Gamboa

1,2

Physics Department, FCT-UNL, Lisbon, Portugal

PLUX Wireless Biosignals S.A., Lisbon, Portugal

Keywords: ECG, Heart Rate Variability, Interactive Tool.

Abstract: The increased masses of data confronting us, originate a pressing need for the creation of a user interface for

better handling and extracting knowledge from it. In this work we developed such a tool for the analysis of

Heart Rate Variability (HRV). The analysis of HRV in patients with neuromuscular diseases, sleep

disorders and cardiorespiratory problems has a strong impact on clinical practice. It has been widely used

for monitoring the autonomic nervous system (ANS), whose regulatory effect controls the cardiac activity.

These patients need to be continuously monitored, which originates data with huge sizes. Our interactive

tool can perform a fast analysis of HRV from such data. It provides the analysis of HRV in time and

frequency domains, and from non-linear methods. The tool is suitable to be run in a web environment,

rendering it highly portable. It includes a programming feature, which enables the user to perform additional

analysis of the data by giving direct access to the signals in a signal processing programming environment.

We also added a report generation functionality, which is extremely important from a clinical standpoint, on

which the evolution in time of relevant HRV parameters is depicted.

1 INTRODUCTION

The ever increasing development of clinical systems

for patients’ biosignals monitoring has given the

clinician and the researcher a way of assessing the

patients’ health state (Silva et al., 2011). The

possibility of drawing relevant medical conclusions

from the analysis of biosignals arises from the fact

that they contain information which is directly

related to the physiological mechanisms that

originated them (Bronzino, 2000).

The constant monitoring of ambulatory patients,

with conditions such as neuromuscular diseases,

sleep disorders or chronic heart problems, has great

value and may provide the clinician with a way for a

more objective therapy (Davenport et al., 2009). It

also plays a preventive role, enabling the clinician to

undertake a faster medical response.

Recordings of several hours originate huge

amounts of data, which need to be processed and

analysed rapidly.

The Heart Rate Variability (HRV) is a very

important tool to analyse ECG signals with detail. In

HRV analysis, the oscillations between consecutive

heartbeats are measured, both in time and frequency

domains.

Since the HRV is strongly associated with neuro-

regulation mechanisms, it also allows one to

evaluate the modulation of the heart rate by the ANS

(Ryan et al., 2011).

The first step of HRV analysis is the

measurement of all the inter-beat intervals, which

can be achieved through the detection of the peaks in

the ECG record, as shown in figure 1.

Figure 1: Highlight of 3 ECG peaks (the R peaks) and

measurement of 2 heartbeat intervals (or RR intervals).

There are several methods of analysis of HRV

that can be divided mainly into Time and Frequency

215

Chorão R., Sousa J., Araújo T. and Gamboa H..

A New Tool for the Analysis of Heart Rate Variability of Long Duration Records.

DOI: 10.5220/0004166602150220

In Proceedings of the International Conference on Signal Processing and Multimedia Applications and Wireless Information Networks and Systems

(SIGMAP-2012), pages 215-220

ISBN: 978-989-8565-25-9

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

domain methods (Camm et al., 1996).

Time domain analysis of HRV allows calculation

of statistical parameters concerning the RR intervals,

tachogram (evolution of the RR interval duration in

time) and instant heart rate representations. These

visual representations are extremely important to

identify disturbances of the cardiac rhythm, such as

arrhythmias or skipped heartbeats.

Frequency domain analysis methods are

particularly useful for analysing long term ECGs.

They are based on power spectral density (PSD)

analysis of RR intervals and provide information

about how power distributes itself among the various

frequencies. The power in different frequency bands

is closely related to different branches of the ANS,

which makes the power distribution a very important

tool to identify ANS related problems.

We present a new tool capable of performing a

HRV analysis from several hour long ECG records.

There are many HRV analysis tools available,

such as “Biopac HRV Algorithm”, “HRVAS: HRV

Analysis Software” (using Matlab) and the “HRV

Toolkit” (PhysioNet). They all provide frequency

and time domain analysis of ECG records. However

they are not interactive or do not allow one to deal

directly with long duration ECGs, which was our

main concern. In our tool, the HRV analysis begins

in the very ECG records, in spite of the available

tools, which assume the ECG peak detection was

made elsewhere. Our tool also surpasses the

currently available tools in what portability is

concerned, since it can be executed in a web

environment.

The analysis takes only a few seconds despite of

the recording length of several hours. Its flexibility

and possibility of a personalized and detailed

analysis make it suitable for both the clinician and

the researcher.

Its user-friendliness makes it pleasant to use and

easy to learn, two very important characteristics in

any software project according to (Holzinger, 2005).

In the following section the programming

architecture of the application is explained and the

features of the developed tool are depicted. Finally,

we discuss some improvements that can be made,

and conclude the work at the end of the paper.

2 HRV ANALYSIS TOOL

We developed a user-friendly and interactive tool

which runs in a web environment, and therefore can

be accessed easily from anywhere with an internet

connection. It gives the user control over powerful

signal processing algorithms, enabling a human-

computer interaction and the exploration of large

datasets comprising biological signals.

The developed tool allows a detailed analysis of

ECG records with several hours. It is coupled to a

previously developed biosignals visualization tool

(Gomes, 2011); (Gomes et al., 2012) making it

possible to simultaneously visualize the portion of

the signal being analysed and the processing results.

The analysis tool also includes a report

generation feature, in which the evolution of the

patient’s heart rate, among other significant

parameters can be tracked. The report is extremely

important from a medical point of view, making the

processing results portable and an extension of the

patient’s clinical file.

For fast and random accessing of processing

results, we used the HDF5 file format, specifically

designed to store and access large datasets (The hdf

group, 2012).

2.1 Acquisition System

The signals were acquired with the aid of a chest

wrap, which integrates respiration, ECG and

accelerometer sensors. They are sampled at 1 kHz

and with a 12 bit resolution and are then sent by

Bluetooth to a mobile phone.

The acquisition process is occurring under

project “wiCardioResp”. This project is developing

technology to remotely monitor patients with

neuromuscular diseases and cardiorespiratory

problems while the patients are comfortably at home

(PLUX, 2012). The acquisitions were carried out

with their agreement, during the night, and last

approximately 7 hours.

2.2 Application Platform

We coupled the HRV analysis tool to a previously

developed web-based biosignals visualization tool.

The application is executed in a web browser,

rendering it highly portable, and giving it the

potential of being accessed from anywhere with an

internet connection.

All signal processing algorithms were written in

Python and used SciPy (SciPy, 2012), a Python

package for scientific computing. The application

platform was created using JavaScript and HTML,

browser-supported languages.

2.3 Programming Architecture

HRV analysis requires the detection of the different

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heartbeats. This is typically achieved using an ECG

peak detection algorithm, because the ECG peak

morphology (see figure 1) makes it an easily

detectable feature in the ECG waveform.

The utilized peak detector was an adaptation of

the Pan and Tompkins algorithm (Pan and

Tompkins, 1985).

To apply an ECG peak detection algorithm over

a long duration record, as a whole, is unfeasible,

because of the amount of memory required to

perform the computation. The strategy employed to

solve this issue consisted in partitioning the record

into several equally sized portions, processing them

individually. This approach gives rise to possible

detection errors in the borders, which we overcame

considering a fixed number of overlapping samples

between consecutive portions. We used an overlap

of 400 ms, carefully chosen after analysing several

ECG records. The bigger the overlapping period, the

longer the processing will last. However, when

dealing with long duration records, the additional

processing time due to the overlapping size is

negligible (at worst a few seconds more in a record

with several hours).

The strategy that we undertook regarding double

peak detections (one of them in an overlapping

region and the other in the beginning of the

subsequent portion of the signal) has a physiological

basis, as following explained:

When a muscle contracts, an action potential is

generated. Then, there is a long absolute refractory

period, which, for the cardiac muscle, lasts about

250 ms. During this period, the cardiac muscle can’t

be re-excited, which results in an inability for heart

contraction (Widmaier, Raff and Strang, 2005).

Thus, we can theoretically consider a maximum

heart rate of about 4 beats per second, or 240 beats

per minute. Under this assumption, considering that

in a signal sampled at 1 kHz, 250 ms would be

represented by 250 samples, and after a thorough

observation of the behaviour of the peak detection

algorithm, we stated that if the same peak were

detected twice, the detection would never occur with

such deviation in samples. Therefore, we

considered that if two R peaks were detected and

separated by less than 50 ms, we were dealing with a

double detection and only considered one of them.

With a tolerance of 50 ms, we ensured that double

detections would not occur on the overlapping

regions, while not dismissing any occurrence of a

physiologically possible RR interval.

The amount of time it takes for the peak

detection to complete depends on several factors: the

sampling frequency at which the signals were

acquired, the signals’ duration, the peak detection

algorithm and the processing unit of the machine in

which the computations take place. It took us about

8 minutes to process a 7 hour long ECG, sampled at

1 kHz on a single 2.2 GHz processor.

The peak detection phase can be regarded as pre-

processing and only has to be done once. Its duration

may be easily decreased applying parallel

programming techniques, due to the embarrassingly

parallel nature of the problem.

The peak detection information was stored in a

.h5 file (HDF5 file extension) for fast and random

access, a necessary feature to make the analysis of

portions of the signal possible.

For analysing a specific part of the ECG, for

instance, between the first and second hour of the

recording, all that has to be done is accessing the

processing results in the .h5 file and select the

samples between the first and second hour. This

information is then analysed in just a few seconds.

The simultaneous visualization of the signal and the

processing results is an extremely important

capability allowing a more detailed and accurate

analysis by the clinician and the researcher.

2.4 Tool Features

The HRV analysis provided is composed of time and

frequency domain parameters and also of some

visual representations, which, allied to the biosignals

visualization tool allow a more detailed data

inspection.

The time and frequency domain parameters

provided are depicted in figure 2.

Figure 2: The most important HRV analysis parameters

provided by our tool.

Besides time and frequency domain parameters,

our tool includes several important visualization

features as well:

 tachogram and instantaneous heart rate (figure

3). Since each point in these representations is

directly associated with a cardiac cycle, we included

a zooming feature, directly synchronized with the

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217

Figure 3: Instantaneous Heart Rate of a 1m30s period from a night recording with a duration of 7h17m.

ECG visualization. This makes the inspection of the

processing results interactive and gives the clinician

the possibility to rapidly identify periods of

arrhythmia or other significant events and observing

them on the ECG record;

• RR Interval and Instantaneous Heart Rate

histograms with a bin size of 1/128 s (standard bin

size according to Camm et al., 1996);

• Power Spectral Density, with highlight of the

relevant frequency bands and zooming feature for a

more detailed analysis. The frequency range of the

different frequency bands is as follows: ULF (0 -

0.003 Hz), VLF (0.003 - 0.04 Hz), LF (0.04 - 0.15

Hz), HF (0.15 - 0.04 Hz);

• Poincaré plot.

Extremely important as well is a report generation

feature. The report includes a global analysis in

which the parameters and representations already

described are presented. It also illustrates the

evolution of those parameters in time.

We also included a programming feature,

particularly focused on the researcher, in a console.

This way, additional signal processing can be done,

using all of the SciPy capabilities (SciPy, 2012). We

loaded several important variables into the

namespace: the sampling frequency, an array with

all the R peaks, in samples, that were detected in the

selected time range, the beginning and ending of the

selected time range, in samples. All these variables

contain the necessary and sufficient information to

enable a personalized analysis by the programmer or

the researcher.

Figure 4: First page of a HRV analysis report from a night

recording which lasts 7h17m. This page presents a global

time and frequency domain analysis.

3 CONCLUSIONS AND FUTURE

WORK

In this work we developed an HRV analysis tool

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218

suited for the analysis of long duration records.

Taking into account all described tool features,

we conclude that it offers a flexible, detailed and

accurate way of analysing long duration ECGs. The

report generation and the programming mode

features are very important and give the tool a

greater flexibility and portability, besides that of a

web-based application.

The zooming capabilities and the synchronism

with a biosignals visualization tool make it highly

interactive and provide a way for better and faster

data discovery.

The user interface allows control over powerful

signal processing algorithms and can be regarded as

a way to allow the non-expert to still utilize them for

clinical or research purposes.

In future work we intend to develop an algorithm

which will allow us to determine the minimum

number of overlapping samples for optimal ECG

peak detection. We also aim to add the functionality

of analysing several records simultaneously and to

generate the corresponding HRV analysis reports

using parallel programming techniques.

Figure 5: Second page of the report showing power

spectral density, histograms and Poincaré plot.

Figure 6: Tachogram hour-by-hour evolution. This

representation is extremely useful for identifying periods

of arrhythmia and other events through the night.

ACKNOWLEDGEMENTS

This work was partially supported by National

Strategic Reference Framework (NSRF-QREN)

under project ”wiCardioResp” and ”AAL4ALL”,

and Seventh Framework Programme (FP7) program

under project ICT4Depression, whose support the

authors gratefully acknowledge. The authors also

thank PLUX, Wireless Biosignals for providing the

acquisition system and sensors necessary to this

work.

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