Classification and Detection of ECG-signals using Artificial Neural Networks

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Abstract
Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for
cardiovascular diseases. A cleaned ECG signal provides necessary information about the
electrophysiology of the heart diseases and ischemic changes that may occur. It provides
valuable information about the functional aspects of the heart and cardiovascular system. The
objective of the thesis is to automatic detection of cardiac arrhythmias in ECG signal.
Recently developed digital signal processing and pattern reorganization technique is used in
this thesis for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the
ECG signal consists of following stages: detection of QRS complex in ECG signal; feature
extraction from detected QRS complexes; classification of beats using extracted feature set
from QRS complexes. In turn automatic classification of heartbeats represents the automatic
detection of cardiac arrhythmias in ECG signal. Hence, in this thesis, we developed the
automatic algorithms for classification of heartbeats to detect cardiac arrhythmias in ECG
signal.QRS complex detection is the first step towards automatic detection of cardiac
arrhythmias in ECG signal. A novel algorithm for accurate detection of QRS complex in ECG
signal peak classification approach is used in ECG signal for determining various diseases . As
known the amplitudes and duration values of P-Q-R-S-T peaks determine the functioning of
heart of human. Therefore duration and amplitude of all peaks are found. R-R and P-R
intervals are calculated. Finally, we have obtained the necessary information for disease
detection .For detection of cardiac arrhythmias; the extracted features in the ECG signal will
be input to the classifier. The extracted features contain morphological l features of each
heartbeat in the ECG signal. This project is implemented by using MATLAB software. An
interface was created to easily select and process the signal. “.dat” format is used the for ECG
signal data. We have detected bradycardia and tachycardia. Massachusetts Institute of
Technology Beth Israel Hospital (MIT-BIH) arrhythmias database has been used for
performance analysis.

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Table of Contents
ABSTRACT …………………………………………………………………………………i
LIST OF FIGURES................................................................................................................iv
LIST OF ACRONYMS..........................................................................................................v
CHAPTER 1
INTRODUCTION..................................................................................................................1
1.1 GENERAL OVERVIEW...............................................................................................1
1.2 THE ELECTROCARDIOGRAM…..............................................................................1
1.3 ECG WAVEFORM DESCRIPITION………………………………………………..4
1.4 OBJECTIVE………………………...…………………………………………………6
1.5 PROBLEM STATEMENT…………………………………………………………….6
1.6 MOTIVATION………………………………………………………………………...7
1.7 ARRHYTHMIA DATASETS…………………………………………………………7
1.7.1. NORMAL SINUS RHYTHM…………………………………………………...8
1.7.2. SINUS TACHYCARDIA………………………….…………………………… 8
1.7.3. SINUS BRADYCARDIA ………………………………………………………9
1.7.4. HEART BLOCK………………………………………………………………...9
CHAPTER 2
LITERATURE SURVEY....................................................................................................10
CHAPTER 3
METHODOLOGY………………………...………………………………………………14
3.1 MATLAB………….…….…………………………………………………………...14
3.2 MIT-BIH ARRHYTHMIAS DATABASE….............................................................16
3.3 PRE-PROCESSING OF ECG SIGNALS …………………......................................16
3.4 INTRODUCTION TO WAVELET………………………. …………..……………19
3.5 QRS DETECTION…………………………………………………………………..24

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CHAPTER 4
CLASSIFICATION AND DETECTION...................................................................…....29
4.1 CONCEPT OF PATTERN RECOGNITION……………….......................................29
4.2 NEURAL NETWORK BASED CLASSIFIER...........................................................31
4.3 ARCHITECTURE OF NEURAL NETWORK………………. ..................................31
4.4 APPLICATION OF NEURAL NETWORK…………………………………….…...33
CHAPTER 5
RESULTS AND CONCLUSIONS……..…………………………………….…………..36
5.1 RESULTS……………………………………………..……………………………..36
5.2 CONCLUSION…………………………….………………………………………..38
5.3 FUTURE SCOPE ………………………….………………………………………..38
REFERENCES ………………………….……………………………………...….……….39
ANNEXURE - A
ANNEXURE - B

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LIST OF FIGURES
Figure1.1 Represents a typical ECG waves ............................................................................2
Figure 1.2 Shape of the electrocardiogram ……………………….………….….…...………3
Figure 1.3 The heart’s anatomy with waveforms …………………….……………..……….3
Figure 1.4 Location of chest electrodes ……………………………...………………………5
Figure 1.5 Normal Sinus Rhythms ..........................................................................................8
Figure 1.6 Sinus Tachycardia ……………………………………………….…….…………8
Figure 1.7 Sinus Bradycardia …………………………………………….………………….9
Figure 1.8 Heart Block ............................................................................................................9
Figure 3.1 Flow-chart of ECG Classification ................................................................…....15
Figure 3.2 Implementation results of preprocessing ..............................................................17
Figure 3.3 Normal ECG waveform .......................................................................................24
Figure 3.4 Implementation result of PQRST detection method ……………………………28
Figure 4.1 Three input of and one output neuron unit ………………...……………………29
Figure 4.2 Simple neural network structure ………………………………………………..30
Figure 4.3 Left side is the feed forward network …..……...……...………………...…..…..32
Figure 4.4 Right side is the feedback network …………………......………………………32
Figure 5.1: GUI Editor……………………………………...……………………………….35
Figure 5.2: Sample GUI ……...………………………………………………….……..…...35
Figure 5.3: GUI showing normal ECG ………………..……………………………………36
Figure 5.4: GUI showing Bradycardia ECG …...……………...……………………………36
Figure 5.7: GUI showing Tachycardia ECG ………………...……………..………………37

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LIST OF ACRONYMS
ECG Electrocardiogram
MIT-BIH Massachusetts Institute of Technology Beth Israel Hospital database
N Normal beat
LBBB Left Bundle Branch Block Beat
RBBB Right Bundle Branch Block Beat
APB Atrial Premature Beat
J Nodal (Junctional) Premature Beat
SPB Supraventricular Premature Beat
PVC Premature Ventricular Contraction
AEB Atrial Escape Beat
NEB Nodal (junctional) escape beat
VEB Ventricular Escape Beat
PB Paced Beat
UB Unclassifiable Beat
MLP-BP Multilayer Perceptron Back Propagation
RBF Radial Basis Function
ANN Artifical Neural Network
WT Wavelet Transform
DWT Discrete Wavelet Transform
CAT Computer-Aided Tomography

Classification and Detection of ECG-signal using ANN
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CHAPTER 1
INTRODUCTION
1.1 General Overview
The population increases day by day. Therefore people don't get enough benefit from
health services. It is impossible for patients to be kept under the supervision of a physician
at all times. Speed and time are very important for physicians. This project is intended to
help doctors to analyze the ECG signal. Doctors will save time thanks to program. This
program can also be used for education.
1.2 The Electrocardiogram
Open and see methods were used for diseases of internal organs by physician. It meant that
people were always suffering. This was also a very difficult situation for both physicians
and patients. Technology has improved. Now doctors use non-invasive methods. For
example urine and blood tests.
Various signals are taken from the organs in the human body. The information generated
by these signals is in this group. For examples EEG (electrical activity of the brain,
electroencephalogram), EMG (electrical activity of the muscles, electromyogram). and
ECG. ECG signal analysis was performed in this project. Detailed information about this
signal is given below.
The electrical current is spread through the body when the heart undergoes depolarization
(The electrical activation of the tissues is positive) repolarization (The discharge of
electrical charge of the tissues). This electrical activity generated by heart. It can be
measured by an array of electrodes placed on the body surface. These records are called
electrocardiograms (ECG or EKG).
Over the past few years, there has been an increased trend toward processing of the
electrocardiogram (ECG) using microcomputers. A survey of literature in this reach area
indicates that system based on microcomputers can perform needed medical services in
extremely efficient manner.

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Figure 1.1: Represents a typical ECG waves.
The heart beat period of time curve (typical ECG waveforms) is examined in figure 1.
There is sharpness in the figure. Periodic sharpness can be seen in figure 1. This sharpness
associated with the contraction of the ventricular. These are called “depolarization”.
Muscle fibers lost resting potential during depolarization. Signal prepared to go slightly
negative deviation as a rule, it continues with a big positive taper. Followed by a second
negative deviation income. The cons traction of the ventricles is called “QRS complex”. A
oscillation is seen after QRS complex. This oscillation also is called ST range or ST wave.
Now the potential difference is immeasurable. A zero line is drawn at this time is called the
isoelectric line. This is followed by T-wave (representing the depolarization). The P wave
indicates that they are in the excited state to the atrium. Finally a U wave is not always
seen. It is typically small. U waves are thought to represent repolarization of the papillary
muscles. An ECG signal can be examine in figure 2 (This is a theoretical signal. The actual
ECG signal is not as smooth as this signal).

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Figure 1.2: Shape of the electrocardiogram
An electrocardiogram is a measurement of the electrical activity of the heart muscle which can be
obtained from the surface of the skin and from different angles like Figure 3. When the heart
muscle contracted and pumping the blood for all parts of body, action potentials will be released
through the mechanical process within the heart muscle which leads to electrical activity.
Figure 1.3: The heart’s anatomy with waveforms from different specific part of the heart.

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1.3 ECG waveform description
As we mentioned earlier ECG, wave is formed as a projection of summarized potential
vectors of the heart. ECG wave has several peaks and "formations", which is useful for its
diagnosis. These are:
• P-wave - indicates the depolarized wave that distributes from the SA node to the
atria, and its duration is between 80 to 100 milliseconds.
• P-R interval - indicates the amount of time that the electrical impulse passing from
the sinus node to the AV node and entering the ventricles and is between 120 to 200
milliseconds.
• P-R segment - Corresponds to the time between the ends of atrial depolarization to
the onset of ventricular depolarization. Last about 100ms.
• QRS complex - Represents ventricular depolarization. The duration of the QRS
complex is normally 0.06 to 0.1 seconds.
• Q-wave - Represents the normal left-to-right depolarization of the inter ventricular
septum.
• R-wave - Represents early depolarization of the ventricles.
• S-wave - Represents late depolarization of the ventricles.
• S-T segment – it appears after QRS and indicates that the entire ventricle is
depolarized.
• Q-T interval - indicates the total time that need for both repolarization and
ventricular depolarization to happen, so it is estimation for the average ventricular
action duration. This time can vary from 0.2 to 0.4 seconds corresponding to heart
rate.
• T-wave - indicates ventricular repolarization and its time is larger than
depolarization.

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Figure 1.4: Location of chest electrodes
The standard 12-channel ECG is obtained using four limb leads and chest leads in six
positions. The right leg is used to place the reference electrode. The left arm, right arm, and
left leg are used to get, I, II, III. A combined reference known as Wilson's central terminal
is formed by combining the left arm, right arm, left leg leads, and is used as the reference
for the chest leads. The augmented limb leads known as aVR, aVL, aVF (aV for the
augmented lead, R for the right arm, L for the left arm, and F for the left foot) are obtained
by using the reference being Wilson's central terminal without the exploring limb lead.
Leads I, II and III are commonly referred to bipolar leads as they use only two electrodes to
derive a view. One electrode acts as the positive electrode while the other as the negative
electrode (hence bipolar) .Chest (precordial) leads are
V1: 4th intercostal space, right sternal edge.
V2: 4th intercostal space, left sternal edge.
V3: between the 2nd and 4th electrodes
.

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V4: 5th intercostal space in the midclavicular line.
V5: on 5th rib, anterior axillary line.
V6: in the midaxillary line.
To make recordings with the chest leads (different electrode), the three limb leads are
connected to form an indifferent electrode with high resistances. The chest leads mainly
detect potential vectors directed towards the back. These vectors are hardly detectable in
the frontal plane . Since the mean QRS vector is usually directed downwards and towards
the left back region, the QRS vectors recorded by leads V1–V3 are usually negative, while
those detected by V5 and V6 are positive. In leads V1 and V2, QRS = -ve because, the
chest electrode in these leads is nearer to the base of the heart, which is the direction of
electronegativity during most of the ventricular depolarization process. In leads V4, V5,
V6, QRS = +ve because the chest electrode in these leads is nearer the heart apex, which is
the direction of electropositivity during most of depolarization.
1.4 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.
1.5 Problem Statement
Recently biomedical signal processing has been a hot topic among researchers. Their most
effort is focused on improving the data analysis of automatic systems. Cardiologists by
using various values which occurred during the ECG recording can decide whether the
heart beat is normal or not. Since observation of these values are not always clear,
existence of automatic ECG detection system is required.

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1.6 Motivation
Cardiovascular diseases are one of the most common causes of death. A patient normally
suspected of cardiac disorder undergoes the test of the electrical activity of the heart.ECG
beat recognition can be done by Artificial Neural Network (ANN). If the ECG signals are
clean and high fidelity then the health information can be understood properly.
1.7 Arrhythmia Datasets
ECG signals are collected from Physionet MIT-BIH arrhythmia database. ECG signals are
described by a text header file (.hea), a binary file (.dat) and a binary annotation file (.atr).
Header file consists of detailed information such as number of samples, sampling
frequency, format of ECG signal, type of ECG leads and number of ECG leads, patient’s
history and the detailed clinical information. In binary data signal file, raw ECG recordings
were sampled at 360 Hz with an 12-bit resolution over the 10 mV range. Binary data file
store raw data signals (12 bits used for each sample). Binary data files are created with
reference to header files. The signal is stored in 212 format. Other files are .atr files
(annotation file). “.atr” files contain some comments for the record files. So which storages
some experts note about the analysis of signal quality results.
ARRHYTHMIA
For a normal healthy person the ECG comes off as a nearly periodic signal with
depolarization followed by repolarization at equal intervals (as figure 2). However,
sometimes this rhythm becomes irregular.
Cardiac arrhythmia is a term for any of a large and heterogeneous group of conditions in
which there is abnormal electrical activity in the heart. The heart beat may be too fast or
too slow, and may be regular or irregular. Arrthymia comes in varieties. It may be
described as a flutter in chest or sometimes “racing heart”. The diagnosis of Arrthymia
requires Electrocardiogram. By studying ECG, Doctors can diagnose the disease and
prescribe the required medications. Most of time arrythmia’s are harmless and happen in
healthy people free of heart disease. However, some abnormal heart rhythms can be serious
or even deadly. Having other types of heart disease an also increase the risk of arrhytmia’s.

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1.7.1 Normal Sinus Rhythm
Figure 1.5: Normal Sinus Rhythm
The first measurement is known as the "P-R interval" and is measured from the beginning
of the upslope of the P wave to the beginning of the QRS wave. This measurement should
be between 0.12 and 0.20 seconds (120-200 ms). Duration of PR is represented “res” in
MATLAB code. When measuring the PR interval, the lead with the longest PR interval
should be chosen. (In some leads, the initial part of the PR interval may be isoelectric. This
may be misinterpreted as a short PR interval) . The R-R intervals are constant; the rhythm
is regular for normal sinus rhythm. Duration of RR interval should be between 480 and 600
ms
1.7.2 Sinus Tachycardia
Figure 1.6: Sinus Tachycardia
Generally, occurs normally in exercises and stress. Other causes may be under lying
medical problems (Anemia, Fever, Blood loss…) Electrical signal is faster than usual. The
heart rate is fast. But heart beats is properly. Therefore RR interval is constant and regular.
Due to the above reasons, duration of RR peak should be shorter than normal sinus (it is
between 0.45 and 0.48 second). PR interval is between 0.12–0.20 seconds.

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1.7.3 Sinus Bradycardia
Figure 1.7: Sinus Bradycardia
Electrical signal is slower than usual. The heart rate is slower. But heart beats is properly.
Therefore RR interval is constant and regular. PR interval is between 0.12–0.20 seconds.
Causes of bradycardia are Sleep, hypothermia; some drugs etc. Duration of RR peak should
be bigger than tachycardia and sinus rhythm.
1.7.4 Heart Block
Figure 1.8: Heart Block
Heart block is an abnormal heart rhythm where the heart beats too slowly. It's slower than
bradycardia. PR interval is bigger than 0.20 second.

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CHAPTER 2
LITERATURE SURVEY
Dr. Sudhir G. Akojwar,Pravin Kshirsagar and Vijetalaxmi Pai “Feature Extraction of EEG
Signals using Wavelet and Principal Component analysis”. In this paper it gives a
comparative analysis along with results of feature extraction of different types of EEG
signals using two best methods of feature extraction they are Wavelet transform method &
Principal component analysis. Electroencephalogram is the record of electrical activity of
the brain. And to classify these disorders we need to extract the features. So this paper
gives successive results with wavelet and PCA for the EEG signals with the disorders with
the epileptic Seizers, slow wave activity, brain death & brain tumors. [1]
Sudhir G. Akojwar, Pravin R. Kshirsagar “A Novel Probabilistic-PSO Based Learning
Algorithm for Optimization of Neural Networks for Benchmark Problems” This paper
approbates a modern and stentorian version of standard particle swarm optimization(PSO)
for optimization of initial weights and biases for multi layer feed forward neural networks
(MLFFNN) with back propagation (BP). The combination of probabilistic-PSO and
MLFFNN sevenfold help in fast convergence of MLFFNN in assortment and sortilege to
various benchmark problems by alienating the imperfection of back-propagation of being
stuck at local minima or local maxima. The propane probabilistic- PSO differs from the
standard PSO in velocity and position parameters. In velocity parameters only particle best
value is make use of for cicerone the particle to gait towards the pursuit in the search space,
while in standard PSO both particle best and global best values are considered for
adjudging the new velocity of the particle. A new parameter introduced which called as the
probability parameter (P0), which adjudges if the standard PSO is that instead of using
same random number, different particles use different random numbers to soar in search
space. The proposed method used to detect the initial weights and biases for MLFFNN with
BP, once the optimum value for initial weights and biases estimated the MLFFNN then
used for classification and sortilege of various neural network benchmark problems. The
benchmarking databases for neural network contain various datasets from various different
domains. All datasets represent sensible issue, which can call diagnosis tasks, and all the

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datasets consist of real world data. The results for accuracy of the proposed probabilistic-
PSO method compared with existing methods. [2]
Nazmy et al described adaptive neuro-fuzzy inference system (ANFIS) algorithm for
classification of ECG wave .The feature extraction is done with the help of Independent
Component Analysis (ICA) and Power spectrum and input is obtained by the RR-interval
of ECG. This paper proposed the classified ECG signals are normal sinus rhythm,
premature ventricular contraction, atrial premature contraction, Ventricular Tachycardia,
Ventricular Fibrillation and Supraventricular Tachycardia .using ANFIS approach the
classification accuracy is also obtained. [3]
Alan and Nikola in presented that use chaos theory for classification of ECG signal and
feature extraction. In this paper consist of phase space and attractors, correlation
dimension, spatial filling index and approximate entropy. The new program is developed
for ECG classification which is based on the chaos method and has developed semi-
automatic program for the feature extraction. The program is helpful to classify the ECG
Signal and extract the feature of the signal. [4]
Castro et al. in paper describe the feature extraction with the help of wavelet transform
technique and gives an algorithm which will utilize the wavelet transform for extracting the
features of ECG signal. This proposed method first denoise by use of soft or hard threshold
then the feature of ECG wave divided in to coefficient vector by optimal wavelet
transformation. In this proposed method choose the mother wavelet transform set of
orthogonal and biorthogonal wavelet filter bank by means of the best correlation with the
ECG signal was developed. After the analysis of ECG signal coefficient are divided as
QRS complex, T wave, P wave then sum to obtain feature extraction. [5]
Wisnu Jatmiko, et al. employed Back-Propagation Neural Network and Fuzzy Neuro
Learning Vector Quantization (FLVQ) as classifier in ECG classification [3]. In their work
they used only the MLII lead as source data. The classes that are considered are Left
Bundle Branch Block beat, Normal beat, Right Bundle Branch Block beat, Premature

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Ventricular Contraction. They used training classification methods namely Back
propagation and FLVQ for their experiment. It provides an average accuracy 99.20% using
Back- Propagation and 95.50% for FLVQ. The result shows that back-propagation leading
than FLVQ and back-propagation has disadvantages to classified unknown category beat
but not for FLVQ. FLVQ has stable accuracy although contain unknown category beat.[6]
Maedeh Kiani Sarkaleh, , proposed a Neural Network based algorithm for classification of
Paced Beat, Atrial Premature Beat arrhythmias as well as the normal beat signal. They
applied Discrete Wavelet Transform for feature extraction and used it along with timing
interval features to train the Neural Network. About 10 recording of the MIT/BIH
arrhythmia database have been used for training and testing the neural network based
classifiers. The model result shows that the classification accuracy is 96.54%. [7]
Karpagachelvi.S, this paper describes an ECG beat classification system using RVM is
proposed and applied to MIT-BIH arrhythmia database to classify five kinds of abnormal
waveforms and normal beats. In feature exacting, the sensitivity of the RVM classifier is
tested and that is compared with ELM. The obtained result confirms the superiority of the
RVM approach when compared to traditional classifiers.[8]
Ruchita Gautam and Anil Kumar Sharma proposed a method based on the Dyadic wavelet
transform technique this method is applied for finding the QRS complex. In this method
focused on the interval of the two consecutive R wave and calculate the heartbeat. This
method is on the ECG waveforms for detect the dieses Ventricular Late Potentials, and
separate the wave P R & T which is associated with features of ECG waveforms. In this
method the main consideration is to find out the R waves and threshold is set to 75% of the
maximum peak. [9]
Manpreet Kaur, A.S.Arora, shows with the help of K-clustering techniques the output
signal is analyzed, the parameter is wave shape, duration and amplitude. With the help of
K-clustering technique minimize the sum of point to centroid distance, this clustered K
summed. In this technique first phase give information about the point are resigned to the

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closest cluster around the centroid. The second phase gives information on line value where
values are self-resigned. The data is taken from MIT-BIH for analysis. The success rate of
classification for set 2, set 3, set 4, set 5 and set 7 is 99.98%, for set 1 it is 87.5% and for set
6 it is 75%. [10]
Maedeh Kiani Sarkaleh and Asadollah Shahbahrami. This paper discuss the Recognition
of cardiac arrhythmias is important for diagnosis of cardiac abnormalies. Several
algorithms have been proposed to classify ECG arrhythmias; however, they cannot perform
very well. Therefore, in this paper, an expert system for Electrocardiogram (ECG)
arrhythmia classification is proposed. Discrete wavelet transform is used for processing
ECG recordings, and extracting some features, and neural network performs the
classification task. Two types of arrhythmias can be detected by the proposed system.
Some recordings of the MIT-BIH arrhythmias database have been used for training and
testing our neural network based classifier. The simulation results show that the
classification accuracy of our algorithm is 96.5% using 10 files including normal. [11]
Pratiksha Sarma, S. R. Nirmala, Kandarpa Kumar Sarma. Heart diseases are frequent
reasons of death. Hence, there is always a need to develop systems that can provide prior
indication about the state of the heart. This is also required because medical facilities may
not be uniform everywhere. In such situation certain innovative approaches using certain
signal processing techniques can provide considerable support. Several algorithms have
been proposed to classify cardiac arrhythmias in the literature; however, many of them fail
to perform optimally. Here, we have proposed a method for ECG arrhythmia classification
using Artificial Neural Network (ANN). Fast Fourier Transform is used for pre-processing
the ECG recordings. Linear Prediction Coefficients (LPC) and Principal Component
Analysis (PCA) are used for extracting some features and then Multi-Layer Perceptron
ANN performs the classification. [12]

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CHAPTER 3
METHODOLOGY
3.1 What is MATLAB?
MATLAB is a numerical computation and simulation tool that was developed into a
commercial tool with a user friendly interface from the numerical function libraries
LINPACK and EISPACK, which were originally written in the FORTRAN programming
language. As opposed to the well-known computer algebra programs, such as MAPLE or
MATHEMATICA, which are capable of performing symbolic operations and, therefore,
calculating with mathematical equations as a person would normally do with paper and
pencil, in principle MATLAB does purely numerical calculations. Nevertheless, computer
algebra functionality can be achieved within the MATLAB environment using the so-called
“symbolic” toolbox. This capability is a permanent component of MATLAB 13 and is also
provided in the student version of MATLAB 13. It involves an adaptation of MAPLE to
the MATLAB language.
3.1.1 Introduction to MATLAB
Computer algebra programs require complex data structures that involve
complicated syntax for the ordinary user and complex programs for the programmer.
MATLAB, on the other hand, essentially only involves a single data structure, upon which
all its operations are based. This is the numerical field, or, in other words, the matrix. This
is reflected in the name: MATLAB is an abbreviation for Matrix Laboratory. As MATLAB
developed, this principle gradually led to a universal programming language. MATLAB is
not just a numerical tool for evaluation of formulas, but is also an independent
programming language capable of treating complex problems and is equipped with all the
essential constructs of a higher programming language. Since the MATLAB command
interface involves a so-called interpreter and MATLAB is an interpreter language, all
commands can be carried out directly. This makes the testing of particular programs much
easier. The elementary MATLAB operations can be divided roughly into five classes:
 Arithmetic operations
 Logical operations

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 Mathematical functions
 Graphical functions
 I/O operations (data transfer)
Figure 3.1: Flow-chart of ECG classification

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3.2 MIT-BIH Arrhythmias database
The MIT-BIH arrhythmia database is used in the study for performance evaluation.
The database contains 48 records, each containing two-channel ECG signals for 30 min
duration selected from 24-hr recordings of 47 individuals. There are 116,137 numbers of
QRS complexes in the database .The subjects were taken from, 25 men aged 32 to 89 years,
and 22 women aged 23 to 89 years and the records 201 and 202 came from the same male
subject. Each recording includes two leads; the modified limb lead II and one of the
modified leads V1, V2, V4 or V5. Continuous ECG signals are band pass-filtered at 0.1–
100 Hz and then digitized at 360 Hz. Twenty-three of the recordings (numbered in the
range of 100–124) are intended to serve as a representative sample of routine clinical
recordings and 25 recordings (numbered in the range of 200–234) contain complex
ventricular, junctional, and supraventricular arrhythmias. The database contains annotation
for both timing information and beat class information verified by independent experts
3.3 Preprocessing of ECG signals
ECG signal inherently contains of various type of unwanted noise and artifact effects like
baseline drift, noise of electrode contact, polarization noise, the internal amplifier noise, noise
due to muscle movement, and motor artifacts. The movements of electrodes induced artifacts
noise. Therefore in order to make the ECG signal ready for feature extraction step, we must
remove baseline wander and eliminate above noise. We propose to use wavelet filtering to filter
the ECG signal since this technique is suitable for computing the R-peak locations without
change of the shape or position of the original signal. According to the previous experimental
knowledge, in order to optimize the signal filtering, we must consider these two criteria: the
signal sampling frequency and the knowledge that most of the noises are located outside of the
frequency interval between 1.5 Hz to 50 Hz . For this purpose, we use a band pass filter which
is constructed by a high pass filter with cutoff frequency 1.5 Hz. This filter eliminates baseline
variations. The output of this filter is cascade with a low pass filter with cutoff frequency 50
Hz. This filter removes high frequency noise. The scale and type of the mother function
parameters are specific to each filter. Thus, the automatic compute of optimal scale for high

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pass filtering when the sampling frequency is 256 is equal to order 6. The optimal scale Order
for the low pass filtering is equal to order 2. The results of above steps are shown in the Figure.
Figure 3.2: Implementation results of preprocessing on record [100] from MIT-BIH
database (a) Original signal, (b) eliminated baseline, (c) noise removal
(a)

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(b)
(c)

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3.4 Wavelet
A wavelet is a wave-like oscillation with a amplitude that starts out at zero, increases, and
then decreases back to zero. It can typically be visualized as a "brief oscillation" like one
might see recorded by a seismograph or heart monitor. Generally, wavelets are
purposefully crafted to have specific properties that make them useful for signal
processing. Wavelets can be combined, using a "reverse, shift, multiply and sum"
technique called convolution, with portions of an unknown signal to extract information
from the unknown signal. For example, a wavelet could be created to have a frequency of
Middle C and a short duration of roughly a 32nd note. If this wavelet were to be convolved
at periodic intervals with a signal created from the recording of a song, then the results of
these convolutions would be useful for determining when the Middle C note was being
played in the song. Mathematically, the wavelet will resonate if the unknown signal
contains information of similar frequency– just as a tuning fork physically resonates with
sound waves of its specific tuning frequency. This concept of resonance is at the core of
many practical applications of wavelet theory.
As a mathematical tool, wavelets can be used to extract information from many different
kinds of data, including – but certainly not limited to – audio signals and images. Sets of
wavelets are generally needed to analyze data fully. A set of "complementary" wavelets
will deconstruct data without gaps or overlap so that the deconstruction process is
mathematically reversible. Thus, sets of complementary wavelets are useful in wavelet
based compression/decompression algorithms where it is desirable to recover the original
information with minimal loss.In formal terms, this representation is a wavelet series
representation of a square-integrable function with respect to either a complete,
orthonormal set of basis functions, or an overcomplete set or frame of a vector space, for
the Hilbert space of square integrable functions.
The word wavelet has been used for decades in digital signal processing and exploration
geophysics. The equivalent French word ondelette meaning "small wave" was used by
Morlet and Grossmann in the early 1980s.

20
3.4.1 Wavelet Theory
Wavelet theory is applicable to several subjects. All wavelet transforms may be considered
forms of time-frequency representation for continuous-time (analog) signals and so are
related to harmonic analysis. Almost all practically useful discrete wavelet transforms use
discrete-time filter banks. These filter banks are called the wavelet and scaling coefficients
in wavelets nomenclature. These filter banks may contain either finite impulse response
(FIR) or infinite impulse response (IIR) filters. The wavelets forming a continuous wavelet
transform (CWT) are subject to the uncertainty principle of Fourier analysis respective
sampling theory: Given a signal with some event in it, one cannot assign simultaneously an
exact time and frequency response scale to that event. The product of the uncertainties of
time and frequency response scale has a lower bound. Thus, in the scaleogram of a
continuous wavelet transform of this signal, such an event marks an entire region in the
time-scale plane, instead of just one point. Also, discrete wavelet bases may be considered
in the context of other forms of the uncertainty principle. Wavelet transforms are broadly
divided into three classes: continuous, discrete and multiresolution-based.
3.4.2 Discrete wavelet transforms (discrete shift and scale parameters)
It is computationally impossible to analyze a signal using all wavelet coefficients, so one
may wonder if it is sufficient to pick a discrete subset of the upper half plane to be able to
reconstruct a signal from the corresponding wavelet coefficients. One such system is the
affine system for some real parameters a > 1, b > 0. The corresponding Discrete subset of
the halfplane consists of all the points (am, namb) with m, n in Z. The corresponding baby
wavelets are now given as
A sufficient condition for the reconstruction of any signal x of finite energy by the formula
is that the functions form a tight frame of L
2
(R).

21
Scaling filter
An orthogonal wavelet is entirely defined by the scaling filter – a low-pass finite impulse
response (FIR) filter of length 2N and sum 1. In biorthogonal wavelets, separate
decomposition and reconstruction filters are defined.
For analysis with orthogonal wavelets the high pass filter is calculated as the quadrature
mirror filter of the low pass, and reconstruction filters are the time reverse of the
decomposition filters.
Daubechies and Symlet wavelets can be defined by the scaling filter.
Scaling function
Wavelets are defined by the wavelet function ψ(t) (i.e. the mother wavelet) and scaling
function φ(t) (also called father wavelet) in the time domain.
The wavelet function is in effect a band-pass filter and scaling it for each level halves its
bandwidth. This creates the problem that in order to cover the entire spectrum, an infinite
number of levels would be required. The scaling function filters the lowest level of the
transform and ensures the entire spectrum is covered.
For a wavelet with compact support, φ (t) can be considered finite in length and is
equivalent to the scaling filter g.
Wavelet function
The wavelet only has a time domain representation as the wavelet function ψ (t). For
instance, Mexican hat wavelets can be defined by a wavelet function.
3.4.3 Applications of discrete wavelet transform
Generally, an approximation to DWT is used for data compression if signal is already
sampled, and the CWT for signal analysis. Thus, DWT approximation is commonly used in
engineering and computer science, and the CWT in scientific research.
Wavelet transforms are now being adopted for a vast number of applications, often
replacing the conventional Fourier Transform. Many areas of physics have seen this
paradigm shift, including molecular dynamics, ab initio calculations, astrophysics, density-

22
matrix localisation, seismology, optics, turbulence and quantum mechanics. This change
has also occurred in image processing, blood-pressure, heart-rate and ECG analyses, brain
rhythms, DNA analysis, protein analysis, climatology, general signal processing, speech
recognition, computer graphics and multifractal analysis. In computer vision and image
processing, the notion of scale space representation and Gaussian derivative operators is
regarded as a canonical multi-scale representation.
One use of wavelet approximation is in data compression. Like some other transforms,
wavelet transforms can be used to transform data, then encode the transformed data,
resulting in effective compression. For example, JPEG 2000 is an image compression
standard that uses biorthogonal wavelets. This means that although the frame is
overcomplete, it is a tight frame (see types of Frame of a vector space), and the same frame
functions (except for conjugation in the case of complex wavelets) are used for both
analysis and synthesis, i.e., in both the forward and inverse transform. For details see
wavelet compression.
A related use is for smoothing/denoising data based on wavelet coefficient thresholding,
also called wavelet shrinkage. By adaptively thresholding the wavelet coefficients that
correspond to undesired frequency components smoothing and/or denoising operations can
be performed.
Wavelet transforms are also starting to be used for communication applications. Wavelet
OFDM is the basic modulation scheme used in HD-PLC (a power line communications
technology developed by Panasonic), and in one of the optional modes included in the
IEEE 1901 standard. Wavelet OFDM can achieve deeper notches than traditional FFT
OFDM, and wavelet OFDM does not require a guard interval (which usually represents
significant overhead in FFT OFDM systems).
3.4.4 Wavelet transforms
A wavelet is a mathematical function used to divide a given function or continuous-time
signal into different scale components. Usually one can assign a frequency range to each
scale component. Each scale component can then be studied with a resolution that matches
its scale. A wavelet transform is the representation of a function by wavelets. The wavelets
are scaled and translated copies (known as "daughter wavelets") of a finite-length or fast-

23
decaying oscillating waveform (known as the "mother wavelet"). Wavelet transforms have
advantages over traditional Fourier transforms for representing functions that have
discontinuities and sharp peaks, and for accurately deconstructing and reconstructing finite,
non-periodic and/or non-stationary signals.
Wavelet transforms are classified into discrete wavelet transforms (DWTs) and continuous
wavelet transforms (CWTs). Note that both DWT and CWT are continuous-time (analog)
transforms. They can be used to represent continuous-time (analog) signals. CWTs operate
over every possible scale and translation whereas DWTs use a specific subset of scale and
translation values or representation grid.
There are a large number of wavelet transforms each suitable for different applications. For
a full list see list of wavelet-related transforms but the common ones are listed below:
• Continuous wavelet transform (CWT)
• Discrete wavelet transform (DWT)
• Fast wavelet transform (FWT)
• Lifting scheme & Generalized Lifting Scheme
• Wavelet packet decomposition (WPD)
• Stationary wavelet transform (SWT)
• Fractional Fourier transform (FRFT)
• Fractional wavelet transform (FRWT)
Discrete wavelets
• Beylkin (18)
• BNC wavelets
• Coiflet (6, 12, 18, 24, 30)
• Cohen-Daubechies-Feauveau wavelet
• Daubechies wavelet (2, 4, 6, 8, 10, 12, 14, 16, 18, 20)
• Binomial-QMF (Also referred to as Daubechies wavelet)
• Haar wavelet

24
• Mathieu wavelet
• Legendre wavelet
• Villasenor wavelet
• Symlet[15]
3.5 QRS detection
Each ECG cycle is consists of a P-wave which is corresponding to the atrial
depolarization, a QRS complex which is corresponding to the ventricular depolarization
and a T wave which is point to the rapid repolarization of the ventricles. A normal ECG
signal and its time intervals are shown.
Figure 3.3: Normal ECG waveform
Most of the clinically features which are useful for diagnostic the disease can be found in
the time interval between components of ECG and the value of the signal amplitude. For
example, the Q-T feature is used to recognition one dangerous disease, the Long Q-T
Syndrome (LQTS), which is responsible of thousand deaths each year. The shape of T
wave is a critical factor and it is essential to identify it correctly since inverted T waves can
be caused as an effect of a serious disease named coronary ischemia.
Designing an algorithm in order to extract the ECG features automatically is very hard
since ECG signal has a time-variant behavior. As a result of these signal properties, we face
with multiple physiological constraints and the existence of noise.

25
In recent years several algorithms have been proposed for detection those features. In they
introduced a method to extract wavelet features and used SVM for classification. In this
thesis, we proposed a method for recognition of time interval and amplitude of various
wave parts of ECG. In the First stage of our approach, the R-peak is detected accurately.
For this purpose we used wavelet. In the second stage, the other ECG components are
identified by using a local search around the detected R-peak. We can summarize this
approach: The location of the R-wave has been identified by using wavelet transform.
Each R-R interval from ECG signal is segmented as follow:
Within an interval, finding the maximum and minimum of the wave which are
corresponding to the Q and S waves. Since P-wave and T-wave is dependant to other
factors; we must provide some deterministic point in order to find their location. These
points are including the end point of S-wave or Soff, the start point of T-wave or Ton, and
the start point of Q-wave or Qon.
3.5.1 R-peaks detection
The detection of R-peak is the first step of feature extraction. For this purpose, we used
DWT due to its ability to recognize different locations of the waves accurately. Similarly to
the preprocessing, we apply the same steps in order to compute the scale and choose the
mother function. We have the QRS complex signal as an input which has the frequencies
between 5Hz and 15Hz, so we select scale of order 4 and choose the Db4 mother wavelet.
The Db4 wavelet is very popular for the detection and location of R peaks due to the strong
similarity of its shape to the ECG signal. Our method is organized in the following steps.
By performing wavelet decomposition, we down sampling the input. Therefore the amount
of unnecessary information is reduced but the component of QRS is not changed.
In order to find the location of R-peak, first we choose the locations which their amplitude
are greater than 60% of the max value of the whole input signal. Since we remove the noise
from the signal in the previous step, it is useful for R-peak detection.
Since we decompose the signal into 4th level, the R-peak location in the modified signal is
at least 0.25 of the R-peak location in the original signal. So in order to find the actual
location of R-peal we must convert the founded positions by multiplying them with 4.

26
Another important point is that R-peak location in modified signal is not exactly on the
original signal at a scale of 4. Position of the signal will change during the down sampling,
so we must to do local search around the R-peaks which calculated in previous part. The
interval of this search can be limited to a window of ± 20 samples.
3.5.2 P, Q and S detection algorithms
The accuracy of detecting R-peak completely affected on P, Q and S detection parts since
their location is determined relatively to R-peak. In the other hand, detect the location of R-
peaks are corresponding to recognize the heart beat interval.
One of the most popular features in ECG signal processing is the R-R interval which can be
computed by the following formula:
R_R (i) = R (i+1) - R (i)
Where R (i) and R (i + 1) are the indexes of the current and next R wave peak respectively.
3.5.3 S wave detection
The S-wave is located on the end of the QRS complex so in order to find its location we
must start from R-peak location plus 6 units because range of the shortest length of it is
between 0.016 and 0.036 seconds. This range is corresponding to 6 and 13 samples. The
stop point of search interval is related to the value of R-R interval.
However the maximum length of the RS intervals is recorded is around 0.27 seconds where
its R-R interval was 1.41 seconds.
3.5.4 Q-wave detection
The Q-wave indicates the start point of the QRS complex section. It is reported that Q-
wave peak location can be found in the range between 0.02 to 0.06 seconds from R-peak.
In the other hand, this interval is equal to 8 and 22 samples. But this interval must be
relevant to the value heart beat length. Therefore Q-R interval varies from one patient to
another, for example a patient with a R-R equal to 235 can have a Q-R interval equal to 19

27
and another one can have Q-R equal to 8 while has a R-R equal to 292. As a result, the
range for search will be larger for longer R-R interval.
3.5.5 P- wave detection
Since P-wave can be located far or near from Q-wave, it is necessary for its interval to be
relative to the R-R interval value.
It is reported that duration of the P-R interval is between 0.09 and 0.19 seconds and this
interval also depends on the R-R interval. This interval is equal to 19 and 38 samples. From
the point of view of proportional, the limits are 14% to 22% of the respective RR range.
One of the benefits of this approach is that we can detect P-waves with low amplitude, so
according to the search area interval.
3.5.6 T-wave detection
Finding T-wave in ECG signal is the most complicated task. Designing a procedure for
detecting T-wave is difficult since it has a time variant behaviors. By checking the ECG
waveform someone can see that the T- wave is located as the interval which has largest
amplitude between S and the middle of the R-R interval. Therefore, the search interval for
T started from S-wave and finished at the middle point of the R-R interval.

28
Figure 3.4: Implementation result of PQRST detection method in (a) record [100], (b)
record [111] from ECG MIT-BIH arrhythmia database.
(a) PQRST detected in record [100].
(b) PQRST detected in record [111].

29
CHAPTER 4
CLASSIFICATION AND DETECTION
4. Classification
In previous chapter, we have introduced the ECG signal and the characteristic of ECG
signal. Now, classification of ECG signal is the main objective in this project, to identify an
input ECG signal to match with our ECG databases. So, an accurate classifier should be
designed and tested. In this chapter, we will discuss about neural network-based classifier.
4.1 Concepts of Pattern Recognition
Before we start to discuss about the neural network, pattern recognition is to recognize a
class of objects, the data about those objects is gathered and then a set of feature
measurements are extracted from those data. Most of the features that we will consider are
the numerical feature, act as input to a mathematical pattern recognizer. The availability of
high speed computers and efficient algorithms make use in computer-aided tomography
(CAT) for image editor or image processing.
Figure 4.1: Three input of and one output neuron unit

30
4.1.1 Brief History of Neural Networks
The neural network (NN) is invented from the idea of network of the biological neuron
model see Figure 3.1. NN start from simple logic function and computer simulation. The
researchers continue to improve the solution of NN-based compu-tational problems such as
pattern recognition. Currently, the NN found in many applications on pattern recognition
and data classification. When 1943, in principle, Warren McCulloch and Walfer Pitts
showed that networks of the artificial neurons could compute any arithmetic or logical
function. This is the origin of neural network field. Donald Hebb (1949) proposed that clas-
sical conditioning is present because of the properties of an individual neurons. The
invention of the perception network and associated learning rule by Frank Rosenblatt
(1958), but the basic perception network could solve only a limited class of a problem.
Although Bernard Widrow and Ted Ho_ (1960) invent a new learning algorithm and to
train an adaptive linear neural network, the problem and limitation of neural network still
exist. So Martin Minsky and Seymour Papert (1969) claimed that further study on neural
network is a dead end and for a decade neural network research was largely suspended.
This condition is changed, when Teuvo Kohonen and James Anderson (1972) invent new
neural network that could act as memories separately to solve the limitation. Stephen
Grossberg (1976) had investigated on the self-organizing network. Most of the limitations
or problems are solved by powerful computer and new ideal.
Figure 4.2: Simple neural network structure that output compare with the target
and feedback to the NN until the output matches the target.

31
4.2 Neural Network-based Classifier
Neural network is the model of information processing. The key element of this model is
the structure of the information processing system. NN can compose a large number of
interconnected elements (neurons) working parallel to solve a problem, through a learning
process. Learning in biological systems involves adjustments to the synaptic connection
that exist between the neurons, which applied to NN as well [SSB96]. The remarkable
ability of NN is to derive meaning from complicated or inaccurate data, which are complex
for human or other computer techniques. Trained NN can easily analysis specify the
category of information and data. Another ability of NN can be defined as the ability to
learn how to do tasks based on the data given for training and create its own organization of
the information it receives during learning time. NN is also able carried out parallel
computing, which improve the hardware design. A neural network is consisted of units
(neurons), arranged in order, which convert input vector into output. Each unit is taken an
input, often is applying a non-linear function to it and transfer the output to the next order
of units.
4.3 Architecture of Neural Networks
In this section, we will discuss some architecture of neural networks and that will used in
this project. So understanding the architecture of NN is important as this project will have
its own pattern of neural network based on those architectures.
4.3.1 Feed-forward Network
Feed-forward NN is defined from its transition direction of information or signal, only one
direction will be the information or signal travel from input to output. There is not feedback
from each output, so the output does not affect the same layer of a neuron. Feed-forward
NN is also used in pattern recognition.

32
4.3.2 Feedback Network
Feedback network is traveling in two directions by including the loops. Feedback network
is powerful but it can be very complicated network. The state of network is changing
continuously until it reached an equilibrium point.
Figure 4.3: Left side is the feed-forward network, data or signal move forward until
it reached the output state.
Figure 4.4: Right side is the feedback network that signal can reverse back to the
previous state.

33
When the input is changed, the state information or data will change until it reached
another equilibrium point. Figure shows that the input, hidden and output layers. The
activity of input units represents the raw information that fed into the network. The activity
of hidden units is determined by the activities of input units and the weight on the
connection between input and hidden units. The activity of output units depends on the
activity of hidden units and the weight between hidden and output units.
4.4 Applications of Neural Network
Neural network is widely applied in various fields and given great improvement in each
field. NN is best at identifying pattern or trends in data, so NN is well suited for prediction
or forecasting in sales forecasting, industrial process control, and data validation and so on.
There are few application examples that related to this project. Signature verification
technique based on two-stage neural network classifier that published by Baltzakis and
Papamarkos, 2001. They had implemented a special two-stage perceptron OCON (one
class one network) classi_cation structure. In the _rst stage, the classifier combines the
decision results of the neural networks and the Euclidean distance obtained using the three
feature sets. The results of the _rst-stage classifier feed a second-stage radial base function
(RBF) neural network structure, which makes the final decision. Feature-based decision
aggregation in modular neural network classifier that published by Wanas et al., 1999. In
modular NN, the individual decisions at its level have to be integrated together using a
voting scheme. All voting schemes use the output of individual modules to produce a
global output without affect the information from the problem feature space. This makes
the choice of the collection procedure very subjective. They focus on making decision
fusion a more dynamic process by integrated learning into voting scheme. Dynamic mean
the collection procedure has the exibility to modify in the input. Minimax classifier based
on NN that published by Alaiz-Rodriguez et al., 2005. The problem of designing a
classifier when prior probabilities are not known or are not representative of the underlying
data distribution is discussed in this paper. They address the problem of designing a neural-
based minimax classifier and propose two different algorithms: a learning rate scaling
algorithm and a gradient-based algorithm. Experimental results show that both succeed in

34
finding the minimax solution and it is also pointed out the differences between common
approaches to cope with this uncertainty in priors and the minimax classifier. A patient
adaptable ECG beat classifier based on NN that published by Gaetano et al., 2009. The
basic idea behind this paper is to consider an ECG digital recording of two consecutive R-
wave segments (RRR interval) as a noisy sample of an underlying function to be
approximated by a fixed number of Radial Basis Functions (RBF). The linear expansion
coefficients of the RRR interval represent the input signal of a feed-forward neural network
which classifies a single beat as normal or ischemic. The system has been evaluated using
several patient records taken from the European ST-T database. Experimental results show
that the proposed beat classifier is very reliable, and that it may be a useful practical tool
for the automatic detection of ischemic episodes.

35
CHAPTER 5
RESULTS AND CONCLUSIONS
5.1 RESULTS
Figure 5.1: GUI Editor
Figure 5.2: Original GUI

36
Figure 5.3: GUI showing normal ECG
Figure 5.4: GUI showing Bradycardia ECG

37
Figure 5.5: GUI showing Tachycardia ECG
FILE Disease
100.dat PERSON IS HEALTY(Normal)
101.dat PERSON IS SUFFERING FROM TACHYCARDIA
104.dat PERSON IS HEALTY
105.dat PERSON IS SUFFERING FROM BRADYCARDIA

38
5.2 CONCLUSION
This study is on detection and classification of arrhythmia beats. The heart beats are
different for different person and all these beats are having different variations with
nonlinear nature. Thus the proposed computerized system will be helpful for early
detection of heart status and to decrease the death percentage of human which occurs due to
the heart disease.
Over the past few years, importance for human life has been known heart disease. ECG is
the main method is used in the diagnosis of heart disease. Purpose of project that the
analysis of heart disease with ECG signals. This project is implemented by using
MATLAB software and on the basis of the wave form obtain the peaks are classified and
are used for the detection of the disease.
The location of the other peaks was determined with location of R peaks. Lastly, we
calculated RR and PR intervals. These intervals are very important for detecting diseases.
These intervals are important for determining the type of illness. For example, electrical
signal is faster than usual for tachycardia. Therefore duration of RR peak should be shorter
than normal sinus. PR interval is also important for us. It was a fixed interval in the
detection of tachycardia and bradycardia.
5.3 FUTURE SCOPE
Automatic cardiac abnormality classification is necessary for real time application. The
classification accuracy can improve by extracting the better features of ECG signal. Future
developments can be made as follows
· To design better feature extraction methodology this can improve the classification result
of cardiac arrhythmias in ECG signal.
· To analyze the classification accuracy using different classifier such that it can classify
the beat arrhythmias in the approved manner.
· To modify the network structure according to cost function of multilayer neural network
so that it can achieve better classification accuracy as compared to existing ECG beat
Classifier.
· Real time operation for recognizing the cardiac arrhythmias can also be done since the
methodology uses the automatic detection of R-peaks and feature extraction techniques.

39
REFERENCES
[1] Pravin Kshirsagar and Sudhir Akojwar, “Hybrid Heuristic Optimization for Benchmark
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George B. Moody And Roger G. Mark
[6] https://www.physionet.org/physiobank/database/mitdb/, 01/02/2017
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[17] T. M. Nazmy, H. El-Messiry and B. Al-bokhity. 2009. “Adaptive Neuron-Fuzzy
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[25] Introduction to graphical user interface (GUI),”The Mathworks”
[26] The wavelet tutorial, second edition part 1, Robi polikar
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Classification”, International Journal of Science, Engineering and Technology Research
,Vol 3, Issue 7, July 2014
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V
Annexure B
function varargout = gui(varargin)
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @gui_OpeningFcn, ...
'gui_OutputFcn', @gui_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
function gui_OpeningFcn(hObject, eventdata, handles, varargin)
handles.output = hObject;
guidata(hObject, handles);
function varargout = gui_OutputFcn(hObject, eventdata, handles)
varargout{1} = handles.output;
function edit_time_min_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'),
get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
%% Load ECG signal
function load_ecg_file_Callback(hObject, eventdata, handles)
[filename,pathname] = uigetfile({'*.mat'},'Load ECG signal');
path = [pathname filename];
if (filename == 0)
disp('Cancel')
else
disp(['ECG loaded: ', fullfile(pathname,filename)])
Struct = load(filename);
handles.filename = filename;
end
guidata(hObject,handles)
% Display ECG signal in time interval
function ecg_time_display_Callback(hObject, eventdata, handles)
view = 8; % on default the first 8 seconds are viewed
filename=handles.filename;

VI
Struct = load(filename);
ecg = Struct.ecg;
fs = 250;
R_i = [];%save index of R wave
R_amp = []; %save amp of R wave
S_i = [];%save index of S wave
S_amp = []; %save amp of S wave
T_i = [];%save index of T wave
T_amp = [];%save amp of T wave
thres_p =[]; %for plotting adaptive threshold
buffer_plot =[];
buffer_long=[]; % buffer for online processing
state = 0 ; % determines the state of the machine in the algorithm
c = 0; % counter to determine that the state-machine doesnt get stock
in T wave detection wave
T_on = 0; % counter showing for how many samples the signal stayed
above T wave threshold
T_on1=0; % counter to make sure its the real onset of T wave
S_on = 0; % counter to make sure its the real onset of S wave
sleep = 0; % counter that avoids the detection of several R waves in a
short time
S_amp1 = []; % buffer to set the adaptive T wave onset
buffer_base=[]; %buffer to determine online adaptive mean of the signal
dum = 0; %counter for detecting the exact R wave
window = round(fs/25); % averaging window size
weight = 1.8; %initial value of the weigth
co = 0; % T wave counter to come out of state after a certain time
thres2_p = []; %T wave threshold indices
thres_p_i = []; %to save indices of main thres
S_amp1_i = []; %to save indices of S thres
thres2_p_i = []; %to save indices of T threshold
Q_i = []; % vectors to store Q wave
Q_amp =[]; %vectors to store Q wave
%% preprocess
ecg = ecg (:); % make sure its a vector
ecg_raw =ecg; %take the raw signal for plotting later
time_scale = length(ecg_raw)/fs; % total time;
%Noise cancelation(Filtering)
f1=0.5; %cuttoff low frequency to get rid of baseline wander
f2=45; %cuttoff frequency to discard high frequency noise
Wn=[f1 f2]*2/fs; % cutt off based on fs
N = 3; % order of 3 less processing
[a,b] = butter(N,Wn); %bandpass filtering
ecg = filtfilt(a,b,ecg);
%% define two buffers
buffer_mean=mean(abs(ecg(1:2*fs)-mean(ecg(1:2*fs)))); % adaptive
threshold DC corrected (baseline removed)
buffer_T = mean(ecg(1:2*fs));%second adaptive threshold to be used for
T wave detection
%% start online inference (Assuming the signal is being acquired
online)
for i = 1 : length(ecg)
buffer_long = [buffer_long ecg(i)] ; % save the upcoming new samples
buffer_base = [buffer_base ecg(i)] ; % save the baseline samples

VII
%% Renew the mean and adapt it to the signal after 1 second of
processing
if length(buffer_base) >= 2*fs
buffer_mean = mean(abs(buffer_base(1:2*fs)-
mean(buffer_base(1:2*fs))));
buffer_T = mean(buffer_base(1:2*fs));
buffer_base =[];
end
%% smooth the signal by taking the average of 15 samples and add the
new upcoming samples
if length(buffer_long)>= window % take a window with length 15
samples for averaging
mean_online = mean(buffer_long); % take the mean
buffer_plot =[buffer_plot mean_online]; % save the processed
signal
%% Enter state 1(putative R wave) as soon as that the mean exceeds the
double time of threshold
if state == 0
if length(buffer_plot) >= 3 %added to handle bugg for now
if mean_online > buffer_mean*weight && buffer_plot(i-1-window) >
buffer_plot(i-window) %2.4*buffer_mean
state = 1; % entered R peak detection mode
currentmax = buffer_plot(i-1-window);
ind = i-1-window;
thres_p = [thres_p buffer_mean*weight];
thres_p_i = [thres_p_i ind];
else
state = 0;
end
end
end
%% Locate the R wave location by finding the highest local maxima
if state == 1 % look for the highest peak
if currentmax > buffer_plot(i-window)
dum = dum + 1;
if dum > 4
R_i = [R_i ind];%save index
R_amp = [R_amp buffer_plot(ind)]; %save index
% Locate Q wave
[Q_tamp Q_ti] = min(buffer_plot(ind-
round(0.040*fs):(ind)));
Q_ti = ind-round(0.040*fs) + Q_ti -1;
Q_i = [Q_i Q_ti];
Q_amp = [Q_amp Q_tamp];
if length(R_amp) > 8
weight = 0.30*mean(R_amp(end-7:end)); %calculate the
35% of the last 8 R waves
weight = weight/buffer_mean;
end
state = 2; % enter S detection mode state 2
dum = 0;
end
else
dum = 0;
state = 0;
end

VIII
end
%% check weather the signal drops below the threshold to look for S
wave
if state == 2
if mean_online <= buffer_mean % check the threshold
state = 3; %enter S detection
end
end
%% Enter S wave detection state3 (S detection)
if state == 3
co = co + 1;
if co < round(0.200*fs)
if buffer_plot(i-window-1) <= buffer_plot(i-window) % see
when the slope changes
S_on = S_on + 1; % set a counter to see if its a real change or just
noise
if S_on >= round(0.0120*fs)
S_i = [S_i i-window-4];%save index of S wave
S_amp = [S_amp buffer_plot(i-window-4)];%save index
S_amp1 = [S_amp1 buffer_plot(i-window-4)]; %ecg(i-4)
S_amp1_i = [S_amp1_i ind]; %index of S_amp1_i
state = 4; % enter T detection mode
S_on = 0;
co = 0;
end
end
else
state = 4;
co = 0;
end
end
%% enter state 4 possible T wave detection
if state == 4
if mean_online < buffer_mean % see if the signal drops below mean
state = 6; % confirm
end
end
%% Enter state 6 which is T wave possible detection
if state ==6
c = c + 1; % set a counter to exit the state if no T wave detected
after 0.3 second
if c <= 0.7*fs
% set a double threshold based on the last detected S wave and
% baseline of the signal and look for T wave in between these
% two threshold
thres2 = ((abs(abs(buffer_T)-abs(S_amp1(end))))*3/4 + S_amp1(end));
thres2_p =[thres2_p thres2];
thres2_p_i =[thres2_p_i ind];
if mean_online > thres2
T_on = T_on +1; % make sure it stays on for at least 3 samples
if T_on >= round(0.0120*fs)
if buffer_plot(i-window-1)>= buffer_plot(i-window)
T_on1 = T_on1+1; % make sure its a real slope change
if T_on1 > round(0.0320*fs)
T_i = [T_i i-window-11];%save index of T wave
T_amp = [T_amp buffer_plot(i-window-11)];%save index
state = 5; % enter sleep mode
T_on = 0;

IX
T_on1 = 0;
end
end
end
end
else
state= 5; % enter Sleep mode
end
end
%% this state is for avoiding the detection of a highly variate noise
or another peak
% this avoids detection of two peaks R waves less than half a second
if state==5
sleep =sleep+c+1;
c = 0;
if sleep/fs >= 0.400
state = 0;
sleep = 0;%look for the next peak
end
end
% update the online buffer by removing the oldest sample
buffer_long(1)=[];
end
end
%% conditions
R_R = diff(R_i); % calculate the distance between each R wave
heart_rate=length(R_i)/(time_scale/60); % calculate heart rate
deepak=mat2str(heart_rate);
if (heart_rate > 70 && heart_rate < 120)
state = 'normal'
elseif (heart_rate < 20)
state = 'Warning ! Heart rate too low';
elseif (heart_rate < 600) && (heart_rate > 240)
state = 'Ventricular Fibrillation';
elseif (heart_rate > 100)
state = 'Tachycardia';
elseif (heart_rate < 60);
state = 'Bradycardia';
else
state = 'Normal';
end
set(handles.bpm_display,'String',heart_rate);
set(handles.anomaly_display,'String',state);
% compute the min max R-R wave
max_R_interval = max(R_R);
min_R_interval = min(R_R);
% detect arythmia if there is any irregularity
% if (max_R_interval/fs)-(min_R_interval/fs) > 0.16
% msgbox('Irregular Rhythm','Arrythmia Detected');
% end
%% plottings
%cla(axes1,'reset');
time = 1/fs:1/fs:view;
R = find(R_i <= view*fs); % determine the length for plotting vectors
S = find(S_i <= view*fs); % determine the length for plotting vectors

X
T = find(T_i <= view*fs); % determine the length for plotting vectors
Q = find(Q_i <= view*fs); % determine the length for plotting vectors
L1 = find(thres_p_i <= view*fs);
L2 = find(S_amp1_i <= view*fs);
L3 = find(thres2_p_i <= view*fs);
if view*fs > length(buffer_plot)
ax(1) =
plot(handles.axes1,time(1:length(buffer_plot)),buffer_plot(1:end));
else
ax(1) = plot(handles.axes1,time,buffer_plot(1:(view*fs)));
end
hold on,scatter(R_i(1:R(end))./fs,R_amp(1:R(end)),'r');
hold on,scatter(S_i(1:S(end))./fs,S_amp(1:S(end)),'g');
hold on,scatter(T_i(1:T(end))./fs,T_amp(1:T(end)),'k');
hold on,scatter(Q_i(1:Q(end))./fs,Q_amp(1:Q(end)),'m');
hold on,plot(thres_p_i(1:L1(end))./fs,thres_p(1:L1(end)),'LineStyle','-
.','color','r',...
'LineWidth',2.5);
hold on,plot(S_amp1_i(1:L2(end))./fs,S_amp1(1:L2(end)),'LineStyle','--
','color','c',...
'LineWidth',2.5);
hold on,plot(thres2_p_i(1:L3(end))./fs,thres2_p(1:L3(end)),'-
k','LineWidth',2);
legend('R wave','S wave','T wave','R adaptive thres','Latest S wave','T
wave adaptive threshold
threshold','Location','NorthOutside','Orientation','horizontal');
ax(2) = plot(handles.axes2,time,ecg_raw(1:(view*fs)));
legend();
zoom on;
traindata = load('traindata.mat');
P = R;
acc = numel(P);
acd = zeros(1,12-acc);
P = [P,acd];
Tc = [1 2 3 4 5 6 7 8 9 10 11 12];
T = ind2vec(Tc);
net = newpnn(P,T);
Y = sim(net,P);
Yc = vec2ind(Y);
ace = sum(Yc);
if (ace == heart_rate)
acf = 0;
accuracy = 100;
sensitivity = 1;
elseif (ace > heart_rate)
acf = 1;
accuracy = 75;
sensitivity = 0.75;
else
acf = 2;
accuracy = 25;
sensitivity = 0.25;
end
set(handles.text19,'String',accuracy);
set(handles.text20,'String',sensitivity);
guidata(hObject,handles)

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