matlab based ecg signal classification
TRANSCRIPT
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014
ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR 1946
MATLAB Based ECG Signal Classification
Jaylaxmi C Mannurmath #1, Prof. Raveendra M #2
#1Department of Electronics and Communication Engineering,
#2Department of Electronics and Communication Engineering,
KLS’s VDRIT, Haliyal-581329, India
Abstract— An electrocardiogram (ECG) is a bioelectrical signal
which records the heart's electrical activity versus time. It is an
important diagnostic tool for assessing heart functions. The early
detection of arrhythmia is very important for the cardiac
patients. ECG arrhythmia can be defined as any of a group of
conditions in which the electrical activity of the heart is irregular
and can cause heartbeat to be slow or fast. It can take place in a
healthy heart and be of minimal consequence, but they may also
indicate a serious problem that leads to stroke or sudden cardiac
death. As ECG signal being non stationary signal, the
arrhythmia may occur at random in the time-scale, which means,
the arrhythmia symptoms may not show up all the time but
would manifest at certain irregular intervals during the day.
Thus, automatic classification of arrhythmia is critical in clinical
cardiology, especially for the treatment of patients in the
intensive care unit. This project implements a simulation tool on
MATLAB platform to detect abnormalities in the ECG signal.
The ECG signal is downloaded from MIT-BIH Arrhythmia
database, since this signal contains some noise and artifacts
hence pre-processing of ECG signal are performed first. The pre-
processing of ECG signal is performed with help of Wavelet
toolbox wherein baseline wandering, denoising and removal of
high frequency and low frequency is performed to improve SNR
ratio of ECG signal. The Wavelet toolbox is also used for feature
extraction of ECG signal. Classification of arrhythmia is based
on basic classification rules. The complete project is implemented
on MATLAB platform. The performance of the algorithm is
evaluated on MIT–BIH Database. The different types of
arrhythmia classes including normal beat, Tachycardia,
Bradycardia and Myocardial Infract (MI) are classified.
Keywords- Db6 , feature extraction, arrhythmia.
I. INTRODUCTION
An electrocardiogram (ECG) is a bioelectrical signal which
records the heart's electrical activity versus time. It is an
important diagnostic tool for assessing heart functions. The
early detection of arrhythmia is very important for the cardiac patients. ECG is a test that measures the electrical activity of
the heart. The signals that make the heart's muscle fibers
contract come from the senatorial node, which is the natural
pacemaker of the heart. In an ECG test, the electrical impulses
made while the heart is beating are recorded and usually
shown on paper, and records any problems with the heart's
rhythm.
ECG arrhythmia can be defined as any of a group of
conditions in which the electrical activity of the heart is
irregular and can cause heartbeat to be slow or fast. It can take
place in a healthy heart and be of minimal consequence, but
they may also indicate a serious problem that leads to stroke
or sudden cardiac death. As ECG signal being non stationary
signal, the arrhythmia may occur at random in the time-scale, which means, the arrhythmia symptoms may not show up all
the time but would manifest at certain irregular intervals
during the day. Therefore, for effective diagnostics, the
variability of ECG signal may have to be observed over
several hours. For this reason, together with the fact that the
volume of the ECG data is enormous, the study is tedious and
time consuming. Thus, automatic classification of arrhythmia
is critical in clinical cardiology, especially for the treatment of
patients in the intensive care unit. The figure 1 shows a
standard ECG signal.
Fig 1 Standard ECG signal
The electrocardiogram is an important tool for providing
information about functional status of the heart. Analysis of
ECG is of great importance in the detection of cardiac
diseases which are the main cause of mortality in many
countries. The Q, R and S wave of ECG signal occur in rapid
succession, do not all appear in all leads and reflect a single
event so are thus normally considered as a whole complex. A
Q wave is any downward deflection after the P-wave.
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014
ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR 1947
An R-wave is an upward deflection and the S wave is any
downward deflection after the R-wave. The QRS complex of
the electrocardiographic signal has the normal duration from
0.06s to 0.1s and provides information about the heart rate, the
conduction velocity, the condition of tissues within the heart
and various abnormalities. Its shape, duration and time of occurrence provide valuable information about the current
state of the heart. Because of its specific shape, the QRS
complex serves as an entry point for almost all automated
ECG analysis algorithms and detection of the QRS complex is
the most important task in automatic ECG signal analysis.
II. DISCRETE WAVELET TRANSFORM (DWT)
The wavelet transform is a convolution of the wavelet
function y (t) with the signal x(t). Orthonormal discrete
wavelets are associated with scaling functions∅(𝑡). The scaling function can be convolved with the signal to pro-duce
approximation coefficients S. The discrete wavelet transform
(DWT) can be written as
𝑇𝑚 ,𝑛 = 𝑥 𝑡 +∞
−∞
𝑦𝑚 ,𝑛 (𝑡)𝑑𝑡
By choosing an Orthonormal wavelet basis 𝑦𝑚 ,𝑛 (𝑡) we can
reconstruct the original signal. The approximation coefficient
of the signal at the scale m and location n can be written as
𝑆𝑚 .𝑛 = 𝑥 𝑡 ∅𝑚 ,𝑛 (𝑡)𝑑𝑡+∞
−∞
But the discrete input signal is of finite length N . So the range
of scales that can be investigated is 0 < m < M. Hence a
discrete approximation of the signal can be written as
𝑥0 𝑡 = 𝑥𝑀 𝑡 + 𝑑𝑚 (𝑡)
𝑀
𝑚=1
where the mean signal approximation at scale M is 𝑥𝑀 𝑡 =𝑆𝑀 ,𝑛∅𝑚 ,𝑛 (𝑡) and detail signal approximation corresponding to
scale m, for finite length signal is given by
𝑑𝑚 𝑡 = 𝑇𝑚 ,𝑛𝑦𝑚 .𝑛 (𝑡)
𝑀−𝑚
𝑛=0
The signal approximation at a specific scale is a combination
of the approximation and detail at the next lower scale.
𝑥𝑚 𝑡 = 𝑥𝑚−1 𝑡 − 𝑑𝑚 (𝑡)
Here we are using Daubechis wavelet algorithm as it is more
efficient as compared to some other algorithms like Haar
algorithm.
III. ECG DATABASE
The database collection is the one of the most important part
of signal processing. We have selected MIT-BIH Arrhythmia
Database directory of ECG signals from physionet. The
source of the ECGs of MIT-BIH Arrhythmia was obtained by the Beth Israel Hospital Arrhythmia Laboratory. The database
contains 48 records divided into two parts first one is of 23
records (numbered from 100 to 124 with some numbers
missing), and another one is contains 25 records (numbered
from 200 to 234, again with some numbers missing).
The database contains approximately 109,000 beat labels.
ECG signals MIT-BIH Database are described by- a text
header file (.hea), a binary file (.dat), a binary annotation file
(.atr) and (.mat) a mat lab file. Header file describe the
detailed information such as number of samples, sampling
frequency, format of ECG signal, type of ECG leads and number of ECG leads, patients history and the detailed clinical
information. In binary data signal file, the signal is stored in
212 formats which mean each sample requires number of
leads times 12 bits to be stored. In the binary annotation file
consists of beat annotations and in the mat lab file data is
available in terms of a row matrix suitable to be loaded in
MATLAB software for further signal analysis
IV. PROBLEM STATEMENT
ECG signal is a continuous monitor of the functioning of heart. Continuous monitoring of ECG signal is very useful in
detecting any functional deviation of the heart. Morphological
changes in ECG wave shape are mapped into commonly
prevailing heart ailments. In this project an attempt is made to
detect the morphological change in ECG wave shape and the
signal is classified into various arrhythmia cases.
V. OBJECTIVE
Objective of the project is to classify the ECG signal into
cases of various arrhythmias. Digital Wavelet Transform
approach is used to derive key parameters of the sampled ECG signal. A MATLAB based GUI is also designed to
present the relevant results in graphical form.
VI. METHODOLOGY
The ECG signal is downloaded from MIT-BIH database since
this signal is having some noise and artifacts having which it
is not advisable to proceed for next stage hence pre-processing
of this ECG signal is of great importance where in the base
line wandering, denoising and removal of high and low
frequency components is performed in this stage. Further the processed ECG signal is given as input to the
feature extraction algorithm where in the features related to
time such as the occurrence and duration of P, Q, R, S and T
waves are determined.
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014
ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR 1948
The extracted parameters are compared with the standard
morphological values of ECG signal based on which decision
taking algorithms are developed and abnormality is classified.
VII. WAVELET SELECTION
The large number of known wavelet families and functions
provides a rich space in which to search for a wavelet which
will very efficiently represent a signal of interest in a large
variety of applications. Wavelet families include
Biorthogonal, Coiflet, Harr, Symmlet, Daubechis wavelets,
etc. There is no absolute way to choose a certain wavelet. The
choice of the wavelet function depends on the application. In
this project Biorthogonal wavelet is used for denoising and Db
6 is used for parameter extraction.
VIII. PEAKS DETECTION
For peak detection we are using online implementation of
state machine logic to determine Peaks and location of various
ECG signal parameters such as peaks and locations of P Q R S
T wave further RR interval, ST segment, PR interval and TT
interval etc are calculated using the basic logic to find distance
between occurrence of the required parameter.
IX. RESULTS
The following figure shows plots for original signal,
Decomposed signal, Denoised signal and Base line wandering removed signal.
Fig 2 Original and Denoised signals
The following figure shows the four levels of decomposed
signal.
Fig 3 Decomposed signals
The following figure shows four reconstruction levels namely
D1, D2, D3, and D4.
Fig 4 First four Reconstruction signals
The following figure shows four reconstruction levels namely
D5, D6, D7, and D8.
Fig 5 Next four Reconstructed signals
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014
ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR 1949
The following figure shows two reconstruction levels namely
A1 and A8.
Fig 6 last two Reconstructed signals
The following figure shows the waveforms for e1, e2 and E3.
Fig 7 Logic signals to find peaks
The following figure shows R, S and T peak detection on processed ECG signal using online Technique.
Fig 8 R, S and T Peak Detected using online Technique
The following figure shows P and Q peak detection on
processed ECG signal using offline Technique.
Fig 9 P and Q Peak Detected using offline Technique
The following figure shows message box which indicates the
heart beat of the ECG signal.
Fig 10 Message box indicating heart rate
The following figure shows GUI of the project which gives
complete analysis on ECG signal.
Fig 11 GUI of the project
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014
ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR 1950
The following table gives information regarding the feature
extraction stage.
Table 1 Results of Feature extraction stage
Data
base
numb
er
RR
Inte
rval
sec
TT
Inter
val
sec
ST
Inter
val
sec
PR
Inter
val
sec
QT
Inter
val
sec
PP
Inter
val
sec
QRS
Inter
val
sec
Std
value
0.6
to
1.2
0.6
to
1.2
0.08
to
0.12
0.12
to
0.2
0.42
to
0.65
0.6
to
1.2
0.08
to
0.12
104 0.81 0.81 0.19 0.88 1.25 0.77 1.07
105 0.71 0.71 0.18 3.82 4.06 0.71 3.88
106 0.82 0.81 0.22 0.79 1.05 0.88 0.84
107 0.92 0.92 0.21 4.71 4.46 0.92 4.26
109 0.72 0.72 0.27 4.52 4.96 0.76 4.70
111 0.81 0.81 0.19 3.31 3.66 0.81 3.47
112 0.72 0.72 0.12 0.13 0.22 0.72 0.10
115 0.80 0.78 0.27 0.18 0.37 0.79 0.10
116 0.72 0.71 0.20 0.11 0.26 0.72 0.07
117 1.07 1.06 0.23 1.15 1.38 0.94 1.14
118 0.73 0.73 0.08 0.1 0.16 0.73 0.08
119 0.89 0.89 0.18 1.39 1.65 0.89 1.47
300 0.66 0.66 0.19 0.14 0.25 0.66 0.06
301 1.07 1.04 0.27 0.21 0.33 1.07 0.08
302 1.00 1.00 0.24 0.11 0.29 1.00 0.05
303 0.71 0.71 0.32 0.25 0.37 0.71 0.05
304 1.13 1.13 0.14 0.21 0.33 1.13 0.19
306 0.92 0.91 0.30 2.47 2.87 0.92 2.58
307 1.10 1.10 0.28 0.15 0.33 1.10 0.05
308 0.66 0.66 0.19 0.14 0.25 0.66 0.06
309 0.74 0.73 0.17 0.22 0.26 0.74 0.10
310 0.67 0.67 0.20 0.18 0.24 0.67 0.05
16265 0.05 0.53 0.25 1.20 1.41 0.21 1.16
18184 0.55 0.53 0.25 1.20 1.41 0.21 1.16
The following table gives information regarding the ECG
signal classification stage.
Table 2 Results of ECG signal classification stage
Data
base
numb
er
RR
Inte
rval
sec
TT
Inter
val
sec
ST
Inter
val
sec
PR
Inter
val
sec
QT
Inter
val
sec
PP
Inter
val
sec
QRS
Inter
val
sec
Std
value
0.6
to
1.2
0.6
to
1.2
0.08
to
0.12
0.12
to
0.2
0.42
to
0.65
0.6
to
1.2
0.08
to
0.12
104 0.81 0.81 0.19 0.88 1.25 0.77 1.07
105 0.71 0.71 0.18 3.82 4.06 0.71 3.88
106 0.82 0.81 0.22 0.79 1.05 0.88 0.84
107 0.92 0.92 0.21 4.71 4.46 0.92 4.26
109 0.72 0.72 0.27 4.52 4.96 0.76 4.70
111 0.81 0.81 0.19 3.31 3.66 0.81 3.47
112 0.72 0.72 0.12 0.13 0.22 0.72 0.10
115 0.80 0.78 0.27 0.18 0.37 0.79 0.10
116 0.72 0.71 0.20 0.11 0.26 0.72 0.07
117 1.07 1.06 0.23 1.15 1.38 0.94 1.14
118 0.73 0.73 0.08 0.1 0.16 0.73 0.08
119 0.89 0.89 0.18 1.39 1.65 0.89 1.47
300 0.66 0.66 0.19 0.14 0.25 0.66 0.06
301 1.07 1.04 0.27 0.21 0.33 1.07 0.08
302 1.00 1.00 0.24 0.11 0.29 1.00 0.05
303 0.71 0.71 0.32 0.25 0.37 0.71 0.05
304 1.13 1.13 0.14 0.21 0.33 1.13 0.19
306 0.92 0.91 0.30 2.47 2.87 0.92 2.58
307 1.10 1.10 0.28 0.15 0.33 1.10 0.05
308 0.66 0.66 0.19 0.14 0.25 0.66 0.06
309 0.74 0.73 0.17 0.22 0.26 0.74 0.10
310 0.67 0.67 0.20 0.18 0.24 0.67 0.05
16265 0.05 0.53 0.25 1.20 1.41 0.21 1.16
18184 0.55 0.53 0.25 1.20 1.41 0.21 1.16
International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, July 2014
ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR 1951
X. CONCLUSION
In this paper, an algorithm based on Wavelet Transform is
presented for the detection of QRS complex of ECG signal.
Wavelet Decomposition of ECG wave up to level 8 using
Daubechis 6 wavelet generates 8 scales of detail coefficients.
Smaller scales correspond to high frequency components and
higher scales correspond to low frequency components of the
signals.
The multi-resolution wavelet transform based system with
different wavelet scales used to find the QRS complex, and
finally the composition of selective confident and thresholding
method shows robust ability of finding QRS compared to
other methods.
Since wavelets are used for pre-processing of ECG signal it is
found that good SNR ratio is obtained using bio-orthogonal
wavelet. An state machine based online detection algorithm is
addressed to detect the peaks of the ECG signal and it is found
that the efficiency of detection is nearly around 98% as the data is compared with MIT-BIH database. Finally the
abnormality that ECG signal is facing is identified using basic
morphological classification technique.
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