The QRS complex detection is one of the most essential tasks in ECG analysis. This paper presents an algorithm of QRS complex detection using morphological operators. The proposed algorithm utilizes the dilation-erosion mathematical morphology filtering to suppress the background noise and remove the baseline drift. Then the modulus accumulation is used to enhance the signal and improve signal-to-noise ratio. The performance of the algorithm is evaluated with MIT-BIH arrhythmia database and wearable ECG Data. The algorithm gets the high detection rate and high speed.

1. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014
INTERNATIONAL JOURNAL OF ELECTRONICS AND
17 – 19, July 2014, Mysore, Karnataka, India
COMMUNICATION ENGINEERING TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 5, Issue 8, August (2014), pp. 200-207
© IAEME: http://www.iaeme.com/IJECET.asp
Journal Impact Factor (2014): 7.2836 (Calculated by GISI)
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IJECET
© I A E M E
PERFORMANCE ANALYSIS OF ECG QRS COMPLEX DETECTION USING
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MORPHOLOGICAL OPERATORS
Ms. Manju S Hadagali1, Mrs. Laxmi Pai2
1(Student, M.Tech, ECE Department, Srinivas Institute of Technology, Mangalore. India)
2(Asst. Professor, ECE Department, Srinivas Institute of Technology, Mangalore. India)
ABSTRACT
The QRS complex detection is one of the most essential tasks in ECG analysis. This paper
presents an algorithm of QRS complex detection using morphological operators. The proposed
algorithm utilizes the dilation-erosion mathematical morphology filtering to suppress the background
noise and remove the baseline drift. Then the modulus accumulation is used to enhance the signal
and improve signal-to-noise ratio. The performance of the algorithm is evaluated with MIT-BIH
arrhythmia database and wearable ECG Data. The algorithm gets the high detection rate and high
speed.
Keywords: Body Sensor Networks (Bsns), Electrocardiogram (ECG), Mathematical Morphology,
QRS Detection.
1. INTRODUCTION
The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since
it reflects the electrical activity within the heart during the ventricular contraction, the time of its
occurrence as well as its shape provide much information about the current state of the heart [1], [2].
Among the three main components known as P, QRS and T waves for a typical ECG, the most
characteristic wave set is the QRS complex recording the depolarization phenomenon of the
ventricles [2]-[4]. The detection of QRS complex is the first step towards analyzing ECG signal.
Therefore, reliable and accurate detection of QRS complex is gaining momentum nowadays.
Recently, much research effort has been devoted into the development and evaluation of QRS
detectors.
The QRS detector is used for analysing QRS complex signal (or ECG signal, is shown in Fig
1). The QRS detector allows doctor to calculate and fine-tune heart beat rate of patients. Among the
noises plaguing the ECG are the power-line interference: 50/60 Hz pickup and harmonics from the
power cord; electrode contact noise: baseline drift due to variable contact between the electrode and

2. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014
17 – 19, July 2014, Mysore, Karnataka, India
the skin; motion artifacts: shifts in the baseline caused by changes in the electrode-skin impedance;
muscle contraction: electromyogram-type signals (EMG); respiration causing drift in the baseline;
and electromagnetic interference and noise coupled from other electronic devices. For meaningful
and accurate these noise sources. Hence, a reliable on-the-fly QRS detection method with low
hardware cost, high sensitivity, and good noise susceptibility is of urgent need.
201
Fig 1: QRS complex with P, Q, R, S, T points
Much effort has been given to the frequency-based ECG filtering algorithms for QRS
detection, i.e., a band-pass filter with a center frequency in the range of 10–17 Hz. After passing the
filter, the signal may be squared or averaged over a number of samples to obtain the place of QRS
waves. But these techniques suffer from the fact that frequency bands of the noise/other components
such as P/T waves overlap with that of QRS complex. In addition, in order to accurately detect the
QRS, prior knowledge of frequency spectrums of different components in the recorded ECG signal is
required through an additional training process.
A new approach to QRS detection is presented in this paper which is based on mathematical
morphology. In this approach one channel is used at a time and the QRS events are detected by the
application of a morphological operator to the ECG signal. This operator works as a peak-valley
extractor [5]. The shape of the signal components (peaks, valleys) that are extracted is determined by
the shape of the structuring clement. This approach constitutes a mathematically formal, accurate,
and robust scheme for QRS detection. Moreover, it is computationally very efficient since it employs
only morphological operations (openings, closings) which are implemented as comparisons
(min/max operators). Morphological operators were developed in the image analysis held but they
are not yet as widely used in one-dimensional (1-D) signal processing tasks. An efficient approach to
impulsive noise suppression and baseline correction in ECG signals using morphological operators is
described in [6].
This remainder of paper is organized as follows. In Section 2, a brief introduction of
mathematical morphology filtering is given, which serves as a basis for the proposed algorithm.
Section 3 presents the new algorithm and discusses the details of the algorithm. What follows is the
VLSI architecture design and implementation in Section 4. The evaluation of the algorithm is done in
Section 5. Conclusion remarks are drawn in Section 6.

3. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014
17 – 19, July 2014, Mysore, Karnataka, India
2. CONCEPTS OF MATHEMATICAL MORPHOLOGY
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Mathematical morphology, which is based on set operations, provides an approach to the
development of non- linear signal processing operators that incorporate shape information of a signal
[5], [10]. In mathematical morphological operations, the result of a set transformed by another set
depends on the shapes of the two sets involved. The shape of a signal is determined by the values
that the signal takes on. The shape information of the signal is extracted by using a structuring
element to operate on the signal.
Morphological operators were developed in the image processing field for machine vision
and medical imaging applications. Morphological operators have been used in a limited way for
background normalization of biological signals. Operators that are used to process the ECG data for
noise suppression and background normalization as described in this paper are known in the image
processing literature as gray-scale morphological operators [11]. There are two basic morphological
operators: erosion and dilation [12]. These operators are usually applied in tandem; opening and
closing are two derived operations defined in terms of erosion and dilation.
Dilation : f Å g(n) = max( f (n − i) + g(i)) (1)
Erosion : fQg(n) = min( f (n + i) − g(i)) (2)
Opening : f o g(n) = ( fQg)Å g(n) (3)
Closin g : f * g(n) = ( f Å g)Qg(n) (4)
Top − hat :That( f (n)) = f (n) − f o g(n) (5)
Bottom − hat : Bhat( f (n)) = f (n) − f * g(n) (6)
where i indicates the th i element in a length L structure element, and g(n) is a predefined structure
element.
3. PROPOSED QRS DETECTION ALGORITHM
QRS complexes are composed of a group of consecutive positive and negative peaks.
Mathematical morphological technology extracts the effective information based on shapes in the
image. It leads to better reproducible results because of the strong mathematical foundation. The
proposed morphology operator filtering plays the most critical role in the proposed algorithm which
removes the noise in ECG signal. Modulus and accumulation further enhance the QRS signal leading
to accurate QRS detection.
The block diagram of the proposed algorithm is shown in Fig. 2. The proposed morphology
operator filtering plays the most critical role in the proposed algorithm which removes the noise and
baseline drift and suppresses the P/T waves in ECG signal. Then the multi-pixel modulus
accumulation is used to enhance the QRS complex. Finally, the threshold is applied to decide the
heart rate. The detailed discussions on each section in Fig. 2 are presented in the following
subsections. The standard MIT/BIH ECG database [13] and our own wearable ECG data are used to
demonstrate the superiority of proposed QRS detector.

4. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014
17 – 19, July 2014, Mysore, Karnataka, India
h ( n ) = f Å g n + fQ g n (7)
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Fig. 2: Block diagram of proposed algorithm
3.1 MORPHOLOGICAL FILTER
Dilation expands an image object and erosion shrinks it. In most applications, opening is used
to suppress peaks while closing is used to suppress pits. Here, in order to detect QRS complex
accurately and quickly, a peak extractor is defined only based on basic dilation and erosion
morphological operators, instead of a series of advanced openings and closings as in existing
literature [5]–[10]. It can be written as
[ ( ) ( ) ]
1
2
v(n) = f (n) − h(n) (8)
The shape (or slope) of the structure element is the most significant factor, herein the smaller
slope performs better in terms of removing noise, but with a larger reduction in the amplitude of
resulted signal. The less significant item is the length of the structure element (i.e., the assumed
duration of the QRS complex), which varies with patient and placement of the electrodes, and longer
structure element turns out to lead to slightly better performance in terms of noise reduction, but with
a small decrease in amplitude of QRS complex. The least significant item is the amplitude of the
structure item, which the filter performance is the least sensitive to. Hence, in our selections, more
considerations are given in terms of the tradeoff between computational complexity, effectiveness,
and amplitude of resulted signal when determining the parameters of structure element. Considering

5. Proceedings of the 2nd International Conference on Current Trends in Engineering and Management ICCTEM -2014
17 – 19, July 2014, Mysore, Karnataka, India
the simplicity and similar shape, we select the triangle structure element by the optimization, defined
as
0.1 Max , Max
3
= =
0.3 , 3 7
T (11)
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[ ]
[ ]
=

6. −
* −
+
*
=
2
,
( 1)
2 1
2
,
( 1)
2
( )
k L
L
A k
k L
L
A k
g k
(9)
L is the length of structure element, A is the amplitude, and k= 1,2,3…………..L.
3.2 MODULUS AND ACCUMULATION
The absolute value of the above output is then combined by multiple-frame accumulation,
which is much alike energy transformation [14], [15]. The energy accumulation process is expressed
as

7. +
[ ]
( ) ( )
s n v i

8. = −
=
2
2
q
n
q
i n
(10)
The value of q should correspond to the possible maximum duration of normal QRS
complex. This step further enhances the filtered ECG signal to make QRS peaks easy to identify.
3.3 THRESHOLD AND DECISIONS
An adaptive threshold is used as the decision function in connection with the proposed
transformation for QRS detection. Usually, the threshold levels are computed signal dependent such
that an adaption to changing signal characteristics is possible. For the signal produced by (9), it is
proposed that the required adaptive threshold is a function of the maximum of the transformed ECG
waveform .The guideline in selecting the threshold is given by
Max Max
=
0.13 Max , Max
7
where Max is determined from the current signal segment which is within the range of
millivolts. The upper and lower bounds of Max will be subject to the selection of structure elements.
4. CIRCUIT IMPLEMENTATION
The design strategy of the hardware implementation is to reduce as much computation load as
possible. The proposed VLSI architecture consists of shift registers, Rom, adder, comparator. The
proposed algorithm can be easily implemented by shift registers, adder, comparator, and some
control logics. The pivotal modules of this algorithm are the dilation and erosion. The opening and
closing operations can be fulfilled by cascading the dilation and erosion units together. The rest parts
of the circuits can be easily implemented with shift registers and adders, and therefore are not
discussed in detail in this paper. The overall implementation of proposed algorithm is verified using
the Xilinx 12.2 Spartan III FPGA. The heart of Spartan-3 is XC3S400 based development board.

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The FPGA operating under universal power supply (110-220 V, 50/60 Hz) provides a +5 V
output with on-board voltage regulation to 3.3 V, 2.5 V, and 1.2 V supply voltage can successfully
detect the QRS complex as expected. The power consumption is much lower than the FPGA [16] or
DSP [17] implementations of other solutions which dissipate at least few milliwatts. The ultra-low
power feature of the proposed QRS detection algorithm helps to extend the battery life, which makes
it extremely suitable for the Body area network devices.
5. RESULTS AND DISCUSSION
Figs 3,4,5,6 represent simulation results of dilation, erosion, peak-valley extractor
and ECG QRS detection respectively. It is obvious that the proposed algorithm correctly
detects the QRS of ECG, even under the presence of several noise, baseline drift and large P/T
waves. Besides being accurate, this approach possesses a number of desirable characteristics:
• It is formal, based on a mathematically well-defined operator.
• The employed PVE operator is a simple, conceivable and efficient mathematical operator that
can be used in signal analysis problems.
• It is computationally very efficient since it employs only openings and closings which are
implemented as min/max operations. The real-time implementation of the proposed QRS
detection scheme is feasible, which is very important in ambulatory patient monitoring.
•
Fig. 3: Simulation results for Dilation
Fig. 4: Simulation results for Erosion

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Fig. 5: Simulation results for Peak valley extractor
Fig. 6: Simulation results for ECG QRS Detection
6. CONCLUSION
The QRS detection is very important to ECG diagnostic analysis. This paper aims to
present a novel algorithm for QRS detection using morphological operators, enhancing the
ECG signal with summation and choosing the best combination of structure elements. The
results of R-peak detection with MIT/BIH standard ECG database show accurate detection
indicating the effectiveness of the proposed approach.
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