FREQUENCY BANDS EFFECTS ON QRS DETECTION

Mohamed Elgendi, Mirjam Jonkman and Friso DeBoer

School of Enigneering and Information Technology, Charles Darwin University, Australia

Keywords: QRS detection, QRS spectrum, ECG.

Abstract: In this paper, we investigate the QRS frequency bands in ECG signals. Any QRS detection algorithm

accuracy depends on the frequency range of ECG being processed. The QRS complex has different

morphology and frequency band for different arrhythmias and noises in ECG signals. A standard bandpass

range that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio will be useful

in ECG monitoring and diagnostic tools. A sensitive QRS detection algorithm has been introduced to

compare the performance of using different frequency bands. The results shows that the recommended

bandpass frequency range for detecting QRS complexes is 8-20Hz which the best signal-to-noise ratio.

1 INTRODUCTION

The electrocardiogram (ECG) is a graphical

representation of the electrical activity of the heart.

ECG signals are obtained by connecting specially

designed electrodes to the surface of the body. It has

been in use as a non-invasive cardiac diagnostic tool

for over a century. The QRS complex is the

dominant feature of the ECG signal. QRS detection

is vitally important in many clinical instruments

such as simple cardio-tachometers, arrhythmia

monitors, and implantable pacemakers. Therefore,

reliable detection of the QRS complex remains an

important area of research. The problem is complex

in that the morphologies of many normal as well as

abnormal QRS complexes differ widely.

The ECG signal is often corrupted by noise from

many sources: 50/60 Hz from power line

interference, EMG from muscles, motion artefacts

and changes in the electrode-skin interface.

Moreover, large and wide P- and T-waves can act as

sources of interference when detecting the QRS

complexes.

Band pass filtering is an essential first step of

nearly all QRS detection algorithms. The purpose of

band pass filtering is to remove the baseline wander

and high frequencies which do not contribute to

QRS complexes detection. In this research we

investigate which pass bands are optimal for QRS

detection.

In literature, the QRS frequency band has been

used without actually identifying the optimum QRS

frequency range for the detection of the QRS

complexes.

Thakor et al. (1983) proposed an estimate of QRS

complex spectra and suggested that the passband

which maximizes the QRS energy is approximately

5-15 Hz. Pan and Tompkins (1985) used cascaded

the low-pass and high-pass filters to achieve a 3 dB

passband from about 5-11 Hz, Cuiwei et al., (1995)

used a quadratic spline wavelet with compact

support and one vanishing moment. Their

conclusion was that most of the QRS complex

energies are at the scales of 2

and 2

. This

corresponds to a frequency range between 8 and

58.5Hz. Sahambi et al. (1997) used the first

derivative of a Gaussian smoothing wavelet and

found that the most of the QRS complex energies are

at the scales of 2

and 2

. They claim that most of

the energy of the QRS complex lies between 3 Hz

and 40 Hz. Benitez et al. (2000) developed a QRS

detection algorithm using the properties of the

Hilbert transform with band stop frequencies at 8

and 20 Hz in order to remove muscular noise and

maximize the QRS complex respectively, Moraes et

al. (2002) combined two improved QRS detectors

using band pass filter between 9 and 30Hz. Chen

and Chen (2003) introduced a QRS detection

algorithm based on real-time moving averaging and

assume the QRS frequencies are concentrated at

approximately 5-15 Hz. Mahmoodabadi et al. (2005)

used Daubechies2 to detect QRS complex using

scales of 2

-2

which is in the frequency range

between 2-40Hz. Most of these authors evaluated

their algorithms using the MIT-BIH database.

Using the QRS detection algorithm described

below, we compare various frequency pass bands to

428

Elgendi M., Jonkman M. and DeBoer F. (2010).

FREQUENCY BANDS EFFECTS ON QRS DETECTION.

In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing, pages 428-431

DOI: 10.5220/0002742704280431

 SciTePress

identify the appropriate frequencies that maximizes

the QRS complex compared to the other ECG

features (P and T waves) and to noise (60 Hz, EMG,

motion artefacts).

2 DATA

Fourty eight ECG records from the MIT-BIH

Arrhythmia database (Moody and Mark, 1990) were

used to test the algorithm. These 30-minutes

recordings were sampled at 360 Hz with a 11-bit rate

resolution over a 10 mV range. Lead I from each

record is used here. No episodes have been excluded

from our analysis.

The MIT-BIH Arrhythmia database is preferable to

other ECG databases for two reasons:

• The MIT-BIH database contains 30-minutes

recordings for each patient which is

considerably longer than the records in other

databases. The CSE database for example

contains 10-seconds recordings only(

J.L.

Willems, 1988)

• The MIT-BIH Arrhythmia database contains

records of normal ECG signals as well as

records of ECG signals that are affected by

non-stationary effects, low signal-to-noise

ratio, premature atrial complexes, premature

ventricular complexes, left bundle blocks, and

right bundle blocks. This provides the

opportunity to test the robustness of the QRS

wave detection method.

3 METHODOLOGY

To compare different frequencies bands that have

been described in literature, for the QRS detection

band pass filter, a sensitive QRS detection algorithm

is needed. The algorithm proposed here consists of

three main stages: bandpass filtering, generating

potential blocks and thresholding.

3.1 Bandpass Filter

Band pass filtering is the first stage of any QRS

detection algorithm. As shown in Table. 1, different

frequency bands have been described in literature to

detect the QRS complex. We investigate here the

optimal frequency bands for accurate QRS detection

in the time-domain. A second order Butterworth

filter with selected pass bands, shown in Table 1, is

used.

h(ECG[n])Butterworts[n]

3.2 Generate Potential Blocks

We demarcate the onset and offset of the potential

QRS waves in the ECG signals by using two moving

averages, based on the normal duration of the QRS

interval which for a healthy adult is 100±20ms (Gari

D. Clifford, 2006).

Table 1: Proposed frequency bands for the detection of

QRS complexes.

Proposed frequency bands in literature

Frequency

Band

(Thakor et al., 1983) and (Chen and Chen, 2003) 5-15Hz

(Pan and Tompkins, 1985) 5-11Hz

(Cuiwei et al., 1995) 8-58.5Hz

(Sahambi et al., 1997) 3-40Hz

(Benitez et al., 2000) 8-20Hz

(Moraes et al., 2002) 9-30Hz

(Mahmoodabadi et al., 2005) 2-40Hz

For a sampling frequency of 360 Hz, the

maximum window size corresponding to the QRS

interval is approximately 44 points and the

maximum window size corresponding to every beat

interval is approximately 231 points. The two

moving averages to detect the R waves are:

0.1 0.2 0.3 0.4 0. 5 0.6 0. 7 0.8 0.9 1

-0.6

-0.4

-0.2

0.2

0.4

0.6

0.8

time(sec)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-0.2

-0.1

0.1

0.2

0.3

0.4

0.5

time(sec)

Figure 1: Demonstrating the effectiveness of using two

moving averages to detect QRS complex (a) filtered one

beat ECG signal with Butterworth bandpass filter (b)

generating blocks of interest after using two moving

averages: the dotted line is the first moving average and

the solid line is the second moving average (c) the

detected R peak after applying the thresholds.

FREQUENCY BANDS EFFECTS ON QRS DETECTION

429

First moving-window integration: the first moving

window integration used to capture the QRS area.

Moreover, the first moving window integration

used as a threshold for the output of the second

moving-window integration, calculated as follows:

y[n]).......2)]-(W-y[n1)]-(W-(y[n

[n]MA

QRS

+++=

where

44=

W which is the window width of QRS

segment. This is shown as the dotted line in Fig.

1(b).

Second Moving-window Integration: The purpose

of the second moving window Integration, shown as

the solid line in Fig. 1(b), is to capture a complete

beat.

y[n]).......2)]-(W-y[n1)]-(W-(y[n

[n]MA

Beat

+++=

where

231=

W is the window width of a complete

heart beat.

When the amplitude of the first moving average

filter (MA

QRS

) is greater than the amplitude of the

second moving average filter (MA

Beat

), that part of

the signal is selected as a block of interest, as

follows:

Fig. 1(b) shows an overview of the result of

applying the two moving averages.

We show one QRS interval in Fig. 1 to

demonstrate the idea of using two filters to generate

blocks of interest. Not all of the blocks are potential

QRS complex. Some block are caused by noise and

need to be eliminated.

3.2.1 Thresholding

The expected size for the QRS interval is based on

the statistics for healthy adults, as described above.

We reject blocks that are smaller than the

expected width of the QRS complex. This

corresponds to:

44KS)width(BLOC <

The rejected blocks are considered as noisy blocks

and the accepted blocks are considered to be

containing R wave.

The maximum absolute value within each

accepted block is considered to be the R peak.

Table 2: QRS detection results for different frequency

bands.

Tested frequency bands SE +P

5-15Hz 97.00% 99.87%

5-11Hz 95.93% 99.81%

8-58.5Hz 97.61% 99.92%

3-40Hz 92.66% 99.87%

8-20Hz 98.31% 99.92%

9-30Hz 97.73% 99.92%

2-40Hz 93.91% 99.80%

Table 2 shows the QRS detection results with

different frequency bands. The frequency range that

optimizes QRS detection is 8-20Hz, first proposed

by Benitez et al (2000). The QRS detection results

of 48 records using this particular frequency band

are shown in Table. 3

Table 3: QRS detection results for a 8-20Hz band pass

filter.

Record

No of

beats

TP FP FN SE +P

100 2273

2272 1 0 100.00% 99.96%

101 1865

1864 1 4 99.79% 99.95%

102 2187

2187 0 0 100.00% 100.00%

103 2084

2084 0 0 100.00% 100.00%

104 2229

2226 3 9 99.60% 99.87%

105 2572

2564 8 22 99.15% 99.69%

106 2027

2027 0 61 97.08% 100.00%

107 2136

2136 0 0 100.00% 100.00%

108 1763

1761 2 109 94.17% 99.89%

109 2532

2532 0 0 100.00% 100.00%

111 2124

2124 0 1 99.95% 100.00%

112 2539

2539 0 0 100.00% 100.00%

113 1795

1794 1 49 97.34% 99.94%

114 1879

1879 0 53 97.26% 100.00%

115 1953

1952 1 1 99.95% 99.95%

116 2412

2406 6 1 99.96% 99.75%

117 1535

1535 0 1 99.93% 100.00%

118 2278

2278 0 5 99.78% 100.00%

119 1987

1987 0 7 99.65% 100.00%

121 1863

1863 0 3 99.84% 100.00%

122 2476

2476 0 0 100.00% 100.00%

123 1518

1518 0 5 99.67% 100.00%

124 1619

1619 0 15 99.08% 100.00%

200 2601 2601 0 42 98.41% 100.00

201 1963 1963 0 86 95.80% 100.00

202 2136

2134 2 15 99.30% 99.91%

203 2980

2936 44 36 98.79% 98.52%

205 2656

2654 2 0 100.00% 99.92%

207 1860

1860 0 61 96.82% 100.00%

208 2955

2954 2 3 99.90% 99.93%

209 3005

3005 0 0 100.00% 100.00%

210 2650

2633 17 5 99.81% 99.36%

212 2748

2748 0 0 100.00% 100.00%

213 3251

3250 1 0 100.00% 99.97%

214 2262

2262 0 10 99.56% 100.00%

215 3363

3362 1 0 100.00% 99.97%

217 2208

2207 1 1 99.95% 99.95%

219 2154

2154 0 31 98.58% 100.00%

220 2048

2047 1 0 100.00% 99.95%

221 2427

2427 0 50 97.98% 100.00%

222 2483

2481 2 47 98.14% 99.92%

223 2605

2605 0 0 100.00% 100.00%

[n]MA

QRS

[n]MA

Beat

THEN

BLOCKS[n] =1

ELSE

BLOCKS[n]

END

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

430

Table 3: QRS detection results for a 8-20Hz band pass

filter.(Continuation).

228 2053

2053 0 101 95.31% 100.00%

230 2256

2256 0 0 100.00% 100.00%

231 1571

1571 0 432 78.43% 100.00%

232 1780

1780 0 449 79.86% 100.00%

233 3079

3078 1 0 100.00% 99.97%

234 2753

2753 0 0 100.00% 100.00%

109493 109397 97 1715 98.31% 99.92%

4 CONCLUSIONS

We compared different frequency bands that have

been proposed in literature for band pass filtering in

order to detect the QRS complex.

The results show that the accuracy of QRS

detection is affected by the selected frequency band.

The QRS detection algorithm was applied to ECG

signals that suffer from a) non-stationary effects, b)

low signal-to-noise ratio, c) atrial premature

complexes d) ventricular premature complexes, e)

left bundle blocks, and f) right bundle blocks.

Analysis of 109493 QRS complexes in 48 records of

MIT-BIH arrhythmia database shows that the

optimal QRS frequency band is 8-20Hz. It is an

optimal band pass filter for QRS detection and it

should be useful in the design of cardio-tachometers,

arrhythmia monitors, and implantable pacemakers.

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