This document discusses a proposed technique for ECG beat classification and feature extraction using artificial neural networks and discrete wavelet transform. The key steps of the proposed technique include ECG data pre-processing using discrete wavelet transform to remove noise, extracting features such as RR interval and QRS complex, designing and training an artificial neural network on the extracted features, and using an Euclidean classifier to classify different ECG cases based on the minimum distance between features. Experimental results on ECG data from the MIT-BIH database show that the proposed technique achieves high classification accuracy and sensitivity compared to previous methods.

1. ECG BEAT CLASSIFICATION AND FEATURE EXTRACTION
USING ARTIFICIAL NEURAL NETWORK AND DISCRETE
WAVELET TRANSFORM.
Submitted to Submitted by:
Prof.Vaibhav Patel Ms. Priyanka Khabiya
Asst.Prof.(CSE) Mtech.Scholar(CSE)
NIRT,Bhopal (M.P.) NIRT,Bhopal(M.P.)
1

2. CONTENTS
 Abstract
 Introduction to Data Mining
 The Human Cardiac Cycle
 Problem Description
 Objective of the Thesis
 Literature Survey
 Proposed Work
 Pre-processing Of ECG Data
 Artificial Neural Networks
 The Minimum Distance Or Euclidean Classifier
 Methodology Of Proposed Work with Flowchart
 Experimental Setup
 Results Analysis
 Conclusion
 References 2

3. ABSTRACT
Data Mining has played a crucial role in various
fields of data analytics, classification and
forecasting. In the proposed work, we have used
data mining of ECG data for the analysis and
classification of ECG data. The steps execution are
ECG data pre-processing, feature extraction and
classification. It has been shown that the proposed
technique yields substantially higher accuracy and
sensitivity compared to previous work. It is hoped
that the proposed technique will provide highly
accurate classification that would reduce chances
of human errors in ECG signal analysis thereby
helping save human life. 3

4. INTRODUCTION
Data Mining is defined as extracting
information from huge sets of data.
The information or knowledge extracted can
be used for any of the following applications:
 Market Analysis
 Biomedical Applications
 Fraud Detection
 Customer Retention
 Production Control
 Science Exploration 4

5. INTRODUCTION
 The electrocardiogram (ECG) signal
provides crucial information about the
functioning of the heart
 Cardiac arrhythmia is the condition of
irregular or abnormal heart speed or heart
beat
 The heart is unable to pump requisite blood
to the body
 A lack of sufficient or regular blood flow may
damage vital organs like the brain, heart,
kidney etc and become fatal 5

6. INTRODUCTION
Challenge:
 The enormous amount of data ECG signal
contains.
 Random fluctuations of the ECG signal.
 Difficulty in detecting clear trends in the behavior
of ECG signals.
 These challenges can be overcome using
sophisticated tools like artificial neural networks.
6

7. THE HUMAN CARDIAC CYCLE
7

8. THE HUMAN CARDIAC CYCLE
 Heart follows a regular pattern for pumping blood
 The regular motion is periodic
 Sodium and Potassium salts in the blood stream
results in the generation of weak electrical signals
called electrocardiogram (ECG) signals
 ECG follows a regular pattern
 The peaks are named as P,Q,R,S,T waves and the
separation between the peaks are named as PR
segment, QT segment, QRS Complex, RR segment
observed
8

9. THE HUMAN CARDIAC CYCLE
 Segments or intervals yield vital information
about the functioning of the heart
 Segments are called ECG features
 Estimation of these features is called
Feature Extraction
 Unprocessed ECG signal contains different
disturbances called noise
 Disturbances need to be removed and
technique is called data pre-processing
9

10. PROBLEM DESCRIPTION
The problem description can be stated as follows:
 ECG signals are extremely random in nature and
generally contain effects of noise and random
fluctuations.
 Smoothening out the effects of noise and fluctuations
prior to feature extraction is challenging.
 Feature values need to be critically computed since they
play a decisive role in further classification of different
ECG cases.
 Due to the above mentioned reasons, obtaining a high
value of sensitivity and accuracy is a serious challenge
for ECG signals. 10

11. OBJECTIVE OF THE THESIS
 The objective of the proposed work is to:
 Effectively pre-process ECG signals prior to feature
extraction.
 Design an accurate feature extraction mechanism that
would accurately extract features exhibiting high
regression (near unity).
 Accurately classify the different ECG categories to attain
high sensitivity and accuracy.
11

12. LITERATURE SURVEY
Title Author Publication Year Findings
A
Patient-
Adaptive
Profiling
Scheme
for ECG
Beat
Classific
ation
Miad
Faezipo
ur et al.
IEEE
Transactions
on
Information
Technology in
Biomedicine
2010 In this paper, Wavelet Transform has
been applied on the ECG signal
before the feature extraction stage.
Wavelet Transform is a mathematical
technique that converts a signal into
different frequency bands.
Frequency band means a range of
frequencies. From theory of wavelet
transform, we come to know that
analysis of highly fluctuating signals
like ECG signals is can be efficiently
done using Wavelet Transform.
Normal Fourier Transform is not
suitable for the analysis of highly
fluctuating signals like ECG signals.
The accuracy of the feature
extraction and classification is
approximately 97.4%

13. Title Author Publicati
on
Year Findings
ECG
arrhythmia
recognitio
n via a
neuro-
SVM–KNN
hybrid
classifier
with virtual
QRS
image-
based
geometric
al features
M.R.
Homaei
nezhad
et al.
Elsevier 2012 In this proposed work, ECG
arrhythmia (a common category of
heart diseases) recognition and
classification is proposed using
Wavelet Transform and a combined
classification technique of Neuro
Support Vector Machine (SVM) and
Knowledge Neural Network (KNN) is
used. The only feature used for
classification is the Q-R-S duration or
the QRS Complex. While the QRS
complex is used for feature
extraction, the hybrid technique is
used for feature classification of the
particular type of disease.
The accuracy of the feature
extraction and classification is
approximately 98.06%

14. Title Author Publicatio
n
Year Findings
Arrhythmia
s Detection
and
Classificati
on base on
Single Beat
ECG
Analysis
Somsan
uk
Pathou
mvanh
et al.
IEEE
Explore
2014 In this proposed technique, feature
extraction is done using Discrete
Cosine Transform (DCT). The DCT is
also a technique similar to the wavelet
transform and is used for the analysis
of highly fluctuating signals. The DCT
has the advantage that its computation
is simpler (less complex) compared
to the Wavelet Transform. The DCT has
the property of concentrating signal
energy to the lower frequency range.
The classification is done on using the
Linear Discriminant Analysis (LDA)
technique which is also a commonly
used classifier technique.
The accuracy of the feature
extraction and classification is
approximately 97.1%

15. Title Author Publicatio
n
Year Findings
Heartbea
t
classifica
tion
using
disease-
specific
feature
selection
Zhanche
ng Zhang
et al.
Elsevier
(Computer
s in
Science
and
Medicine)
2014 In this technique, peak detection
technique is used. Subsequently
feature extraction is implemented.
There is a modification that is done to
the signal before feature extraction. The
signals peaks or peak durations are
flatted out before feature extraction. For
classification, the support vector
machine (SVM)is used.
The SVM is a discriminative classifier
i.e. it discriminates or separates or
classifies different values or classes
once supervised training is provided.
Supervised learning or training is
estimating the neural network function
from the data provided to the neural
network.
The accuracy of the feature
extraction and classification is
approximately 98.98%

16. PROPOSED WORK
 The proposed work contains various steps which are
mentioned sequentially:
 Data mining of ECG data.
 Pre-processing of ECG data.
 Feature extraction of ECG data.
 Designing an Artificial Neural Network and training it
with extracted feature values.
 Using the Euclidean Classifier to classify the different
ECG cases.
 A comprehensive description of each of the above
steps is given in the forthcoming slides. 16

17. PRE-PROCESSING OF ECG DATA
 We have used the Discrete Wavelet Transform
(DWT) to remove the irregularities in the ECG
signal
17

18. PRE-PROCESSING OF ECG DATA
The mathematical description of the
wavelet transform can be given by:
C (S, P) =
Here S stands for scaling
P stands for position
t stands for time shifts.
C is the Continuous Wavelet Transform
(CWT) 18

19. PRE-PROCESSING OF ECG DATA
 The scaling function can be defined as:
 WΦ (Jo, k) =
 The Wavelet function can be defined as:
 =
19

20. PRE-PROCESSING OF ECG DATA
 The Haar Wavelet
 The Symlet
 The DB Wavelet
20

21. ARTIFICIAL NEURAL NETWORKS
 Highly Non Linear Structure
 Highly Parallel Structure
21

22. ARTIFICIAL NEURAL NETWORKS
 The structure can be mathematically modeled as:
 Here X represents the signal
 W represents the weight
 Ɵ represents the bias
22

23. ARTIFICIAL NEURAL NETWORKS
23

24. ARTIFICIAL NEURAL NETWORKS
 Concept of Back Propagation
24

25. THE MINIMUM DISTANCE OR EUCLIDEAN
CLASSIFIER
 Euclidean Distance defined as:
 Here x, y and z belong to a vector space C
 Let the separation of a new sample from the
mean value of a particular category be Di
 We estimate Min (Di) to categorize the data into
classes
25

26. THE MINIMUM DISTANCE OR EUCLIDEAN
CLASSIFIER
 True Positive (TP): when a sample belongs to
category and predicts its belongingness
 True Negative (TN): when a sample does not
belong to category and predicts its non-
belongingness.
 False Positive (FP): when a sample does not
belong to category and predicts its
belongingness.
 False Negative (FN): when a sample belongs to
category and predicts its non-belongingness.
26

27. THE MINIMUM DISTANCE OR EUCLIDEAN
CLASSIFIER
 Sensitivity (Se): It is mathematically defined as:
 Accuracy (Ac): It is mathematically defined as:
27

28. METHODOLOGY OF PROPOSED WORK
 Step1. Load ECG data (source MIT.BIH database)
 Step2. Pre-Process Signal using Discrete Wavelet Transform
 Step3. Compute features like RR interval, QQ interval, QRS
Complex after proper thresholding.
 Step4. Design an artificial neural network (ANN) employing
back propagation (LM)
 Step5. Train the network for most significant feature value,
(RR peak interval in this case)
 Step6. Check Regression and Mean Square Error for the
training.
 Step7. Evaluate epoch where the error reduces down and
training stops.
 Step8. Evaluate Accuracy and Sensitivity using most
prominent feature.
28

29. 29

30. EXPERIMENTAL SETUP
 The experimental setup for the proposed technique
requires loading ECG data into the MATLAB
application.
 MATLAB (Matrix Laboratory) has been chosen as
the tool for the Simulation since it contains several
in built mathematical functions and effective in-built
graphics for ease of complex computation.
 Here MATLAB-14 has been used as the Simulation
Tool.
30

31. RESULT ANALYSIS
 Original ECG Signal
31

32. RESULTS
 ECG Signal after Filtering
32

33. RESULTS
 Wavelet Decomposition of Original ECG Signal
at Sym5
33

34. RESULTS
 Smoothening Effect of DWT
34

35. RESULTS
 DWT pre-processing of ECG Signal
35

36. RESULTS
 Design of Neural Network
36

37. RESULTS
 Training Parameters
37

38. RESULTS
 Training Values
38

39. RESULTS
 Regression Plots
39

40. RESULTS
 Gradient States
40

41. RESULTS
 Training Epochs
41

42. RESULTS
42

43. RESULTS
 Error Histogram while Training
43

44. CONCLUSION
 Proposed technique is efficient in feature
extraction and classification and attains high
levels of sensitivity and accuracy.
 It can be attributed to the fact that data is
pre-processed using DWT to smoothen out
fluctuations
 Better training yields accurate results as
seen from the regression and error plots
 The sensitivity and accuracy values validate
the efficiency of the proposed technique
44

45. REFERENCES
 [1] Abdelhaq Ouelli, Belachir Elhadadi, Belaid bouikhalene,” Multivariate
Autoregressive Modelingfor Cardiac Arrhythmia Classification Using
Multilayer Perceptron Neural Networks” 2014 , IEEE,978-1-4799-3824-
7/14.
 [2] Somsanuk Pathoumvanh, Kazuhiko Hamamoto, Phoumy
Indahak,”Arrhythmias Detection And Classification Base
On Single Beat Ecg Analysis”, 4th Joint International Conference on
Information and Communication Technology, Electronic and Electrical
Engineering (JICTEE-2014)
 [3] Rashad Ahmed, Samer Arafat,”Cardiac Arrhythmia Classification
Using Hierarchical Classification Model”, 2014 6th International
Conference on CSIT.
 [4] Pathrawut Klaynin, Waranyu Wongseree, Adisorn Leelasantitham
,Supaporn Kiattisin,”An Electrocardiogram Classification Method
Based On Neural Network”,2013 Biomedical Engineering International
Conference (BMEiCON-2013).
45

46.  [5] Nurul Hikmah Kamaruddin, M.Murugappan, Mohammad Iqbal
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Development.
 [6] Manab Kumar Das, Student Member, IEEE, Dipak Kumar Ghosh,
Samit Ari, Member, IEEE,” Electrocardiogram (Ecg) Signal
Classification Using S-Transform, Genetic Algorithm And Neural
Network”, 2013 IEEE 1st International Conference on Condition
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46

47. o [9] A. Amann, R. Tratnig, and K. Unterkofler, “Detecting ventricular
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47