Wavelet Transform Based Neural Network Algorithm

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I nternational Journal Of Scienti f ic Research And Education ||Volume||2||Issue|| 7||Pages 1223-1234|||July-2014|| ISSN (e): 2321-7545

Website: http://ijsae.in

Sindhu M et al I JSRE Volume 2 Issue 7 July 2014 Page 1223

Wavelet Transform Based Neural Network Algorithm For Detection And

Characterization of ECG Signal

Authors

Sindhu M1, Shilpa Biradar

2, S. G.Hiremath

3

1Department of Electronics and Communication, EWIT, Bangalore, Karnataka, India

2 Department of, Information Science, EWIT, Bangalore, Karnataka, India

3Professor and HOD, Department of Electronics and Communication, EWIT, Bangalore, Karnataka, India

[email protected], [email protected], [email protected]

ABSTRACT

The aim of this study is to develop an algorithm to detect and classify types of electrocardiogram (ECG)

signal beats including normal beats (N), Abnormal Beats(Ab) using a neural network classifier. In order to

prepare an appropriate input vector for the neural classifier several preprocessing stages have been

applied. Dyadic wavelet transform (DyWT) has been applied in order to extract features from the ECG

signal. Moreover, Principal component analysis (PCA) is used to reduce the size of the data. Finally, the

processed data has to be detected using emerging neural network modeling methods. It proposes the

different neural network which includes emerging Back propagation neural network methods, to detect and

characterize different ECG and other bio signals to come up with a simple method having less

computational time and without compromising with the efficiency. The characterized signal describe the

extracted features are normal or abnormal ECG data. The proposed classifier has the ability of recognizing

and classifying ECG signals with 100% accuracy.

Keywords- Electrocardiogram (ECG), Dyadic wavelet transforms (DyWT), Back Propagation Neural Network.

1.INTRODUCTION:

Electrocardiogram (ECG) is a diagnosis tool that reflects the electrical activity of heart recorded by placing

skin electrode. The study of the ECG has been widely used for detecting many cardiac diseases.

Cardiovascular disease, including heart disease and stroke, remains the leading cause of death around the

world. Because it has a time varying structure and is focused on physiological changes in patients and ECG

signal they are not always the strongest signal due to noise. The ECG is very week signal, ranging from

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5mV to 10µV, with a frequency from 0.05Hz to 100Hz. One cardiac cycle in an ECG signal consists of the

P-QRST waves, of different intervals, amplitudes and frequencies. Each cardiac cell is surrounded by and

filled with solutions of Sodium (Na+), Potassium(K+), and Calcium(Ca++). When an electric impulse is

generated in the heart, the interior part becomes positive with respect to the exterior. This change of polarity

is called Depolarization. After depolarization the cell comes back to its original state. This phenomenon is

called repolarization. The most heart attacks and strokes could be prevented if some method of pre-

monitoring and prediagnostic can be provided. In particular, early detection of abnormalities in the function

of the heart, called arrhythmias, can be valuable for clinicians.

The electrocardiogram (ECG) plays an important role in the process of monitoring and preventing heart

attacks. In the last decade many approaches to QRS detection have been proposed, involving artificial neural

networks, real time approaches, genetic algorithms, and heuristic methods based on nonlinear transforms

and filter banks .In figure 1 describe the ECG signals P interval-QRS interval and T interval detection of

interval in ECG signal is more complex. Purposely QRS wave is used to detect arrhythmias and identify problems in regularity of heart rate. These waves correspond to the far field induced by specific electrical

phenomena on the cardiac surface, namely, the atrial depolarization, P, the ventricular depolarization, QRS

complex, and the ventricular repolarization, T. Sometimes, in an ECG signal, QRS complexes may not

always be the significant waves because they change their structure with respect to time for different

conditions, so that consider always be the strongest signal interval in an ECG signal. In order to address this

problem, the analysis of ECG signals for detection of electrocardiographic changes have been carried out

using techniques such as autocorrelation function, time frequency analysis, wavelet transform (WT) and

neural network [1].

Figure 1 ECG waveform and QRS region interval

The identification of QRS complex is tough task because P or T wave have similar wavelet transform based

QRS complex detector using dyadic wavelet transform, as QRS and ECG signal can be affected and

degraded by other sources such as noise. Wandering due to respiration, patient movement, interference of

the input power supply, contraction and twitching of the muscles and weak contract of ECG electrodes.




Therefore, it is essential for the QRS detector to avoid the noise. The QRS region is a structure on the ECG

that corresponds to the depolarization of the ventricles. Because the ventricles contain more muscles than the

atria, the QRS region is larger than the P wave. Therefore QRS detectors must be invariant to different noise

sources and should be able to detect QRS region even when the morphology of the ECG signal is varying

with respect to time. The each sample of QRS region passed through wavelet transform based QRS region

detector using Dyadic wavelet transform algorithm.

The ECG signal has to be processed using wavelet transform method to characterize the ECG data and

classify using networks architectures [2]. Application of wavelet transforms principal component analysis

(PCA) and neural network structures in order to detect and classify different kinds of heart arrhythmias. In

the proposed system, emerging neural network structures used in order to find the for the classification of

specific types of ECG signal have been compared. A neural network classifier to detect and classify the

ECG signal. They also used PCA as a method to select and extract features from the ECG signal used

different types of multilayer neural network as a classifier to detect two types of ECG patterns. ECG beatclassifier performance by using a combination of dyadic wavelet transform (DyWT) and principal

component analysis (PCA) [4] in order to prepare a more effective input data for neural network classifier,

leading to better classification results.

Principal components analysis (PCA) is a well-established technique for feature extraction and

dimensionality reduction and has been used in a wide range of ECG signal analysis [3].

Dyadic wavelet transform (DyWT) is a time frequency analysis method which differs from the more

traditional short time Fourier transform (STFT) by having a variable window width.

Back Propagation Neural network detects and classify the effective input data provided by PCA and DyWT.

2. METHODOLOGY:

The ECG signal has to be processed using wavelet transform method to characterize the ECG data and

classify using networks architectures. ECG signal, the electrical interpretation of cardiac muscle activity, is

very easy to interfere with different noises while gathering and recording. The wavelet transform is the most

promising method to extract features from ECG signals [2] and [3]. These features characterize the behavior

of the ECG signals. A comparison between different structures for heart arrhythmia detection algorithms

based on, PCA, wavelet transform and neural network is carried out.

WAVELET TRANSFORM

The Wavelet Transform is a very promising technique for time frequency analysis. By decomposing signal

into elementary building blocks that are well localized both in time and frequency, the WT can characterize

the local regularity of signals. This feature can be used to distinguish ECG waves from serious noise,

artifacts and baseline drift. Therefore wavelets are used to extract the significant information from the ECG

signal. The WT is a powerful tool of signal processing for its multi resolution possibilities. Unlike the




Fourier transform, the WT is suitable for application to non-stationary signal, whose frequency response

varies in time. In practice, the Dyadic Wavelet Transform (DyWT) is computed by passing a signal

successively through a high pass and a low pass filter. For each decomposition level, signal x convolve with

low pass filter coefficient and with high pass filter coefficient.

To calculate a dyadic wavelet transform, a mother wavelet is first selected and then dilated by powers of

two. Mother wavelet and its dyadic dilates are convolved with original function to produce a sequence of

functions termed as the dyadic wavelet transform.

Let us ψ indicates mother wavelet which is a function with zero mean. The dyadic dilation at level j is given

by

Ψ2 j j Ψ 2 j)

The wavelet transforms f(x) at the dyadic scale j and at the location x is defined to be

W2 j f f *Ψ2

j ∫ ft.Ψ2 j(x-t)dt

The dyadic wavelet transform is then the sequence of functionsWf=(W2

j f(x)) j€z

And W is the dyadic wavelet transform operator.

The wavelet transform gives fine time resolution at high frequency and poor frequency resolution at low

frequencies, the wavelet transform gives good frequency resolution and poor time resolution.

Consider a discrete 1D signal s of length N which has to be decomposed into wavelet coefficients c.

The Dyadic (Fast) wavelet transform consists of log2 N steps at most. The first decomposition step takes the

input and provides two sets of coefficients at level 1: approximation coefficients cA1 and detail coefficients

cD1.The vector s is convolved with a low-pass filter for approximation and with a high-pass filter. Dyadic

decimation follows which down samples the vector by keeping only its even elements. Such down sampling

will be denoted by↓ in block diagrams. The coefficients at level j + 1 are calculated from the coefficients

at level j, which is illustrated in the bottom-left figure 2.

This procedure is repeated recursively to obtain approximation and detail coefficients at further levels. This

yields a tree-like structure of filters called filter banks. The structure of coefficients for level j = 3 is

illustrated in the bottom-right figure 2.

Figure 2 Dyadic Wavelet Transform filter banks

The Dyadic Inverse Wavelet Transform takes as an input the approximation coefficients cAj and detail

coefficients cDj and inverts the decomposition step. The vectors are extended (up sampled) to double length




by inserting zeros at odd-indexed elements and convolving the result with the reconstruction filters.

Analogously to down sampling, up sampling is denoted ↑ in the figure 3.

Figure 3 Dyadic Inverse Wavelet Transformations

ARTIFICIAL NEURAL NETWORK

Artificial neural networks (ANN) have been trained to perform complex function in various fields of

application including pattern recognition, identification, classification, speech, vision and control system. In

ANN, knowledge about the problem is distributed in neurons and connections weights of links between

neurons [4]. The neural network must be trained to adjust the connection weights and biases in order to

produce the desired mapping. At the training stage, the feature vectors are applied as input to the network

and the network adjusts its variable parameters, the weights and biases, to capture the relationship between

the input patterns and outputs.

Figure 4 Neural Network adjust system

A neural network is trained to perform a particular input leads to a specific target output. Such a situation is

shown in figure 4 there; the network is adjusted, based on a comparison of the output and the target, until

the network output matches the target. ANNs are widely used in the biomedical field for modeling, data

analysis and diagnostic classification. The most frequently used training algorithm in classification problems

is the back propagation (BP) algorithm.

A perceptron is a type of Neural Network which computes a weighted sum of its inputs, and puts this sum

through a special function, called the activation, to produce the output. A perceptron decides whether an

input belongs to one of two classes (denoted by AbN and N). A typical multi-layer perceptron consists of the

input layer, the output layer and one or more hidden layer of neurons in between as shown in figure 5.

Every neuron or processing element receives weighted input signals which sums up and compares to a given

threshold, by which its activation is determined. The summed up result is passed through a logistic transfer




function, which results in the output signal of each processing element. The training of the multi-layered

perceptron’s is dominated by the so called back propagation algorithm.

Figure 5 A typical multi-layer perceptron neural network

C .PROPOSED METHODOLOGY

The schematic block diagram of the proposed system for the ECG beats classification. The first stage is pre-

processing stage including four levels of data processing which are signal filtering, sample selection, feature

extraction, and finally dimensionality reduction. The other stages are main process and classification of ECG

beats.

PRE-PROCESSING STAGEIn this pre-processing stage a method of signal filtering presented and applied to remove noise. A raw ECG

signal, which has been degraded by other sources such as wandering due to respiration. It is obvious that the

noise has been removed, leading to a better performance of the neural classifier. Filter design use integer

coefficients resulting in faster computation. Time scale representation of signal obtained using digital

filtering techniques. Resolution of signal is changed by filtering operation.

Sample selection selects each value of time scale signal of high frequency components and extracts the peak

within the range. Phase detection extracts more information and detect high frequency component of QRS

region. In phase detection how many cycles are generated and each cycle are compared with other cycles.

Extract the useful data from samples [5]. The data of ECG signals are taken from the neuro center, including

normal beats and abnormal arrhythmia beats.

Fourier transform provides only frequency information and time information is lost. Dyadic wavelet

transform provides both time and frequency information but resolves all frequency equally. Wavelet

transform provides good time resolution and poor frequency resolution at high resolution and good

frequency resolution and poor time resolution at low frequency. Dyadic wavelet transform has been selected

for feature extraction. The Dyadic Wavelet Transform (DyWT) is computed by passing a signal successively




through a high pass and a low pass filter. The analysis shows that extracted features from ECG signal by

using Dyadic wavelet transform would be suitable than the other methods in detecting ECG feature and

appropriate for data classification and computed coefficients can represent difference very well. To compute

Dyadic wavelet of signal a specific range of scales which is suitable and appropriate for feature extraction is

needed. DyWT has same number of coefficients as original signal. Dyadic Wavelet Transform is selected

because its smoothing feature was suitable for detecting changes of the ECG signals. In the classification

stage, the proposed wavelet energy distribution features are applied as input to NN.

Principal Component Analysis acts as a dimensionality reduction component. Huge amount of data is not

efficient to performance pattern recognition process. Computed matrices of wavelet coefficients has been

selected and arranged as neural network classifier input vector.

Principal component leads to dimensionality reduction, without significant loss of data information is

achieved, leading to a better performance of neural.

Main Process Neural network input is most important component of designing neural network based pattern classification;

since even the best classifier will perform poorly if the input is not selected well. Neural network consists of

single layer of neurons where output of each neuron is given as input to all other neurons.

Classification

Neural Network is a powerful pattern recognition tool. It is defined as software algorithms that can be

trained to learn the relationships that exist between input and output data, including nonlinear relationships.

In this back propagation neural network algorithms is used to detect and characterize ECG signal. A back

propagation network consists of at least three layers (multi-layer perception) an input layer, at least one

intermediate hidden layer, and an output layer. Typically, input units are connected in a feed-forward

fashion with input units fully connected to units in the hidden layer and hidden units fully connected to units

in the output layer. An input pattern is propagated forward to the output units all the way through the

intervening input-to-hidden and hidden to- output weights when a Back Propagation neural network(BPNN)

is cycled. Feed-Forward Neural Network (FFNN) is used to classify different ECG signals. In this proposed

system the sigmoid transfer function (a special case of the logistic function) for the output and the hidden

neurons and the linear function for the input ones figure 6. Also this algorithm also applied to detect edge

detection and image compression and passed through the neural network. The main objective is to come up

with a simple method having less computational time and without compromising with the efficiency.

Transmission of ECG often results in the corruption of signal due to introduction of noise. The process of

extracting the required components while rejecting the background noise is called Enhancement of ECG

signal.




Figure 6 A typical neuron and the sigmoid transfer function.

Neural network input is more important component of designing neural network based pattern classification;

since even the best classifier will perform poorly if the input is not selected well. Neural network consists of

single layer of neurons where output of each neuron is given as input to all other neurons.

Neural network is ready as a classifier to detect and classify class of ECG signal into an normal or abnormal

ECG beat. The classifier has been tested by 1000 coefficients from each group of ECG signals [6]. These

testing of segments processed and prepared exactly like input vector of neural network, this means that all 4

levels of preprocessing stages such as signal filtering, sample selection, feature extraction, dimensionality

reduction (PCA) have been applied to each segment in order to prepare it as testing segment. These

segments are used to test and evaluate by neural network.

Figure 7 Block diagram of ECG signal classification

3. RESULTS

Neural network is readily acts as a classifier to detect and classify different types of ECG signal in to two

classes of ECG beat groups. In the proposed system neural network uses pattern classifier to detect &

classify normal and abnormal state of ECG signal. The task of pattern classification is to assign an input

pattern (like a speech waveform or hand written symbol) represented by a feature vector to one of many




prespecified classes. The application includes character recognition, speech recognition, ECG beats

classification is shown in figure 8, blood cell classification, and printed circuit board inspection.

Figure 8 ECG Pattern Classifications

Figure 9 Peak Detection of Filtered ECG signal

Figure 9 represents the detection of QRS region in ECG signal using dyadic wavelet transform, blue spikes

shows exact location of scaled QRS complex, red spikes gives the ECG peaks and green spikes indicate

shifted phase of QRS. Also obtain amplitude of the peak in mille volt at Y axis and time occurrence of peak

in at X axis. After training epochs, a lower mean square error (MSE) and a smaller gradient were achieved

using the training set. The ratio of output verses the target shows the regression rate i.e., best fit of ECG data

in figure 12 Also obtain amplitude of the peak of output at y axis and target occurrence of peak in at x axis.

Figure 10 Epoch of Network




Figure 11 Epoch of Network

Figure 12 Regression plot

Figure 13 Recognition of selected a query ECG signal

4. CONCLUSION

The detection of the ECG signal is an important component in the diagnosis of heart diseases but it is

usually corrupted with unwanted interference. To reduce noise, emerging dyadic wavelet transform have

been applied and neural networks have widely been used for successful modeling of ECG signals with low

SNR. Neural Network is applied for ECG signal characterizing, noise reduction, image restoration and

reconstruction. The neural network uses Back propagation algorithm for training. The efficiency of the

BPNN algorithm depends on the choice of learning rate and the momentum. As ECG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic




classification. In this paper, an algorithm for classification of ECG signal based on WT has been proposed.

DyWT with the MRA is applied to decompose ECG signal at resolution levels of the components of the

ECG signal and to extract the percentage distribution of energy features of the ECG signal at different

resolution levels. The FFNN classifies these extracted features to identify the ECGs type according to the

percentage distribution of energy features. The results showed that the proposed classifier has the ability of

recognizing and classifying ECG signals with 95% efficiency. The most important advantage of the

proposed method is the reduction of data size as well indicating and recognizing the main characteristics of

signal. Furthermore, it can reduce memory space, shorten pre-processing needs, the network size and

increase computation speed for the classification of an ECG signal.

REFERENCES

[1]Heart Arrhythmia detection Using Continuous Wavelet Transform and Principal Component Analysis

with Neural Network Classifier, P. Ghorbanian, A. Ghaffari, A. Jalali, C. Nataraj.[2] ECG pattern recognition and classification using non-linear transformations and neural networks: a

review.Maglaveras N1, Stamkopoulos T, Diamantaras K, Pappas C, Strintzis M.

[3] A wavelet optimization approach for ECG signal classification AbdelhamidDaamouchea,

LatifaHamamib, NaifAlajlanc, FaridMelgania.

[4] ECG Analysis Using Wavelet Transform and Neural Network M.M.Sadaphule1, Prof. S.B. Mule 2,

Prof.S.O.Rajankar3 1, 2, 3 Department of ECE, Sinhgad College of Engineering, Pune, India.

[5] Artificial Neural Networks: A tutorial, Anil K. Jain Michigan State University, Jianchang Mao K.M.

Mohiuddin IBM Almaden Research Center.

[6] Mu-Song Chen, Ph.D. Thesis ―Analysis And Design of the Multi-Layer Perceptron Using Polynomial

Basis Functions‖ the University of Teas At Arlington December 99.

[7] K. Swarup, “Economic dispatch solution using hopfield neural network,” IEI Journal -EL., vol. 84,

2004.

[8] A.S. Deepak Mishra and K. Kalra, “Or -neuro based hopfield neural network for solving economic load

dispatch problem,” Neural information processing -letters and reviews., vol. 10, 2006.

[9] Beale, Mark, and Howard Demuth. "Neural network toolbox." For Use with MATLAB, User's Guide,

The Math Works, Natick (1998). pp 1-6.

First Author: Sindhu M, final year Mtech in Digital Electronics, Department of Electronics and

Communication, EWIT, Bangalore-91, Karnataka, India.

Second Author: Shilpa Biradar, Asst Professor in Department of Information Science Engineering, Dr.AIT,

Bangalore, with over 8 years of work experience in the field of engineering education. Her area of interest in

research areas of Signal processing, Neural Networks.

http://www.ncbi.nlm.nih.gov/pubmed?term=Maglaveras%20N%5BAuthor%5D&cauthor=true&cauthor_uid=9848416


http://www.ncbi.nlm.nih.gov/pubmed?term=Stamkopoulos%20T%5BAuthor%5D&cauthor=true&cauthor_uid=9848416

http://www.ncbi.nlm.nih.gov/pubmed?term=Diamantaras%20K%5BAuthor%5D&cauthor=true&cauthor_uid=9848416

http://www.ncbi.nlm.nih.gov/pubmed?term=Pappas%20C%5BAuthor%5D&cauthor=true&cauthor_uid=9848416

http://www.ncbi.nlm.nih.gov/pubmed?term=Pappas%20C%5BAuthor%5D&cauthor=true&cauthor_uid=9848416

http://www.ncbi.nlm.nih.gov/pubmed?term=Diamantaras%20K%5BAuthor%5D&cauthor=true&cauthor_uid=9848416

http://www.ncbi.nlm.nih.gov/pubmed?term=Stamkopoulos%20T%5BAuthor%5D&cauthor=true&cauthor_uid=9848416





Third Author: Dr. S G Hiremath, is a highly skilful professor with over 15 years of experience in the field of

engineering education. He has been awarded Ph D from Anna University in the faculty of ECE. His interest

in research areas of Instrumentation, Digital Signal Processing, Neural Networks, Control Engineering,

Hardware Design, Electronics Circuit Design, Instrumentation Sensors, Process Controls, PLC s and

SCADA, Modelling & Simulation, Neural Networks, Telecom and related Teaching and promotional areas.

He is awarded doctorate for his Research in the areas of Bio Medical Signal processing, Electronic circuit

design, Chemical sensors, and Neural Network modelling/Simulation. He has more than 20 research

publications at peer reviewed international journals and conferences. He is one of the main resource persons

in the fields of Electronics, Especially in domains like Medical Signal Processing, Embedded System

Design, Mathematical modelling and simulation, etc. He has given keynote address in various international

and national conferences all over India. He has organised various workshops, Seminars and Conferences in

the field of Signal processing and System Designing. At present he is working as Professor in the

department of Electronics and Communication Engineering in East West Institute of Technology,Bengaluru, India. He is also consultant for companies in Bengaluru, Pune, Hyderabad, Chennai etc. His

project consultancies includes in the field of Signal Processing, Neural Networks and Embedded Systems

Designing.

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Wavelet Transform Based Neural Network Algorithm