Noise Cancellation of ECG Signal Using Adaptive and Backpropagation Neural Network Algorithms

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 4, Issue 5, May 2014)

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Noise Cancellation of ECG Signal Using Adaptive and

Backpropagation Neural Network Algorithms

Ipsita Dash

1

, Kumar Biswal

2 1, 2

Department of Electronics & Instrumentation Engineering, ITER/SOAUniversity, Bhubaneswar, India.

Abstract-- Now medical treatments are supported by computerized process. Biomedical signal recorded from the human body give many valuable information about the human body organ’s biological activities. These signals are time varying and non-stationary in nature. But many time these biomedical signals are contaminated with drift and interferences caused by bioelectric phenomena, or by power line interference, or by instrument’s noise, or it may be by electrode-skin contact. Noise cancellations of ECG signals are very important. So, In this paper we utilize the LMS, NLMS, RLS and BPNN algorithm for noise cancellation and analysis of ECG signals and compare all four result.

Keywords-- ECG Signal, Adaptive Filter Algorithm, LMS, NLMS, RLS, BPNN, MSE.

I. INTRODUCTION

ECG signals are pseudo-periodic signals means that the cardiac cycle repeats according to the heart rate. However the heart rate may not remain constant. This signals change their properties statistically over time. We also says that this ECG signals or heart wave are non-stationary signals [1]. Noise cancellation of ECG signal is very important because it contains much valuable information about human body organ like heart. If this heart wave contaminated by noise or interference then the valuable information contain by this are corrupted and lost. So, we can’t get the proper ECG signal. To get the proper signal noise cancellation is necessary.

In this paper Adaptive Filter Algorithm like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS) and Back propagation (BPNN) algorithm are used for noise cancellation of ECG signal. These methods are best used in case of signal conditioning. In this algorithm the system parameter or filter coefficient are changes and give the error free output.

II. ADAPTIVE FILTER AND BACKPROPAGATION

ALGORITHM

A system is said to be adaptive in nature when it tries to adjust its filter coefficient to getting some well define goal or desired output [2]. The system can adjust itself to get the desired output. These adaptive filters are self designing filter based on the algorithm used.

The algorithm which used for weight update is learning the initial input and then removes the unwanted noise to give a noise free output. Fig.1 shows the adaptive filter principle.

s = d + n e

x (n) y = n’

Fig.1 Principle of Adaptive Filter

Here, s is the input signal which is the combination of desired signal d and noise signal n. If this noise n is a known quantity then we can directly subtract the value from s and we get the desired output. But if it is an unknown quantity then we can estimate a noise signal n’ which is generate by using some filter and a noise source x (n) which is linearly related with the noise signal n. If the estimated noise n’ is very close to the noise n then we can get our desired output signal d and the noise is cancel. As we know that adaptive filter have two parts one is digital filter and other is adaptive algorithm by using which it adjust its coefficient. Here we use different type of adaptive algorithm like Least Mean Square (LMS), Normalised Least Mean Square (NLMS) and Recursive Least Square (RLS). By using this algorithm we can adjust the filter coefficient and get error free result.

A. LMS Algorithm

Least Mean Square (LMS) algorithm are a class of adaptive filter which used to mimic a desired filter by finding the filter coefficient which give the Least Mean Square of the error signal. LMS algorithm is stochastic gradient descent method where the filter is adapted based on the current time error. The weight of the system is updated by using mean square error and also errors are minimized [2, 3, 5].

Digital Filter

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( ) ( ) ( ) ( )

( ) ( ) ( )

Where ( ) estimated error ( ) is input sequence and μ is appropriate step size.

( ) (( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

Where n = 0, 1, 2...

B. NLMS Algorithm

It is also a class of adaptive filter. The main disadvantages of LMS algorithm is that it is sensitive to the scaling of the inputs which hard to choose a stable learning rate for the algorithm. The NLMS algorithm can solve this problem by normalizing the power of the mean square error and input [3, 4,]. Here the weights are update by using equation

( ) ( )

‖ ( )‖ ( ) ( )

( ) ( ) _{‖ ( )‖} ( ) ( )

( ) ( )

‖ ( )‖ ( ) ( )

Where, n = 0, 1, 2...

( ) ( ) ( )

C. RLS Algorithm

In Recursive Least Square (RLS) algorithm can adjust the filter coefficient recursively which minimize the error most effectively. The weight are updated by using equation

( ) ( ) ( ) ( )

Where,

( ) ( ) ( ⁄ ( ) ( )

( ) _{( ) ( )}

Where, λ is a small positive constant which is very close to, but smaller than 1 [3, 4].

D. Backpropagation Algorithm

Backpropagation is a non-linear extension of LMS algorithm. This Backpropagation algorithm is an iterative method which is derives by using chain rule. Backpropagation input layer have one input layer, one output layer and one or more than one hidden layer. This Backpropagation propagate the instantaneous square error i.e.

Where,

This is backward from output layer to input layer through hidden layer. This process is repeated until all training set associate with input and desired output

( ) and ideally we reached at a local minimum of the unknown square error surface. Fig.2 shows a structure of 3 hidden layers BPNN [5].

x1 s1(y1)

x2 s2(y2)

. .

xi

sj(yj)

Input Layer Hidden Layer Output Layer

Fig.2 Neural Network Structure

From fig.2 jth neuron belongs to output layer then

( ) [ ( )] ( ) ( )

Where,

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International Journal of Emerging Technology and Advanced Engineering

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If the rth neuron belong to the middle hidden field in feedforward topology then

( ) [∑

] ( ) ( )

In this manner we recursively compute the generalized

delta term

from equation 12.

III. EXPERIMENTAL RESULTS

In this paper we take a data sheet from physionet.com which is in .mat format. By using that data sheet in MAT lab we generate a ECG signal. Then a random noise signal is generated and added with the ECG signal. Noise added ECG signal is our input signal and the original ECG signal which is generated from data sheet is our desired output signal. Then by writing MAT lab coding using all algorithm maintain above we eliminated the noise and compeer the output with our desired signal which is generate from the data sheet. Finally we compeer all the result and get which algorithm have give batter result and properly cancel the noise from the ECG signal.

Fig.3 Experimental Result for LMS, data file 100_ECG_0_20.mat

In the above figure 3 shows that first signal is our original signal which is generated from the data file name as 100_ECG_0_20.mat, which we consider as our desired ECG signal, second one is the white Gaussian noise signal generated by using the noise equation given above, third one is the addition of original signal and noise signal which we consider as our input signal, fourth one is the de-noising output signal using Least Mean Square (LMS) algorithm.

Fig.4 Experimental Result for NLMS, data file 100_ECG_0_20.mat

In the above figure 4 shows that first signal is our original signal which is generated from the data file name as 100_ECG_0_20.mat, which we consider as our desired ECG signal, second one is the white Gaussian noise signal generated by using the noise equation given above, third one is the addition of original signal and noise signal which we consider as our input signal, fourth one is the de-noising output signal using Normalised Least Mean Square (NLMS) algorithm.

Fig.5 Experimental Result for RLS, data file 100_ECG_0_20.mat

In the above figure 5 shows that first signal is our original signal which is generated from the data file name as 100_ECG_0_20.mat, which we consider as our desired ECG signal, second one is the white Gaussian noise signal generated by using the noise equation given above, third one is the addition of original signal and noise signal which we consider as our input signal, fourth one is the de-noising output signal using Recursive Least Square (RLS) algorithm.

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Fig.6 Experimental Result for Backpropagation algorithm, data file 100_ECG_0_20.mat

In the above figure 4.5 shows that first signal is our original signal which is generated from the data file name as 100_ECG_0_20.mat, which we consider as our desired ECG signal, second one is the white Gaussian noise signal generated by using the noise equation given above, third one is the addition of original signal and noise signal which we consider as our input signal, fourth one is the de-noising output signal using Backpropagation algorithm.

Fig.7Comparison graph for MSE of LMS, NLMS, RLS and Backpropagation Algorithm

The above figure shows the comparison of mean square error (MSE) in different algorithm. The first bar is the MSE in LMS algorithm; second one is for NLMS algorithm; third one is for RLS algorithm and fourth is for Backpropagation algorithm. Between these four algorithms Backpropagation is batter because the mean square error is less in Backpropagation algorithm.

IV. CONCLUSION

All the four algorithm mention above like LMS, NLMS, RLS and Backpropagation algorithm were simulated and tested for ECG signals. As a result we found that the Backpropagation algorithm is better for cancellation of noise in ECG signal. It removes the 60 Hz noise and the mean square error is also least in Backpropagation algorithm.

REFERENCES

[1] Investigation of adaptive filtering for noise cancellation in ECG signals, Soroor Behbahani , Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran, pp 144-149, 0-7695-3039-7/07 $25.00 © 2007 IEEE

[2] A. Bhabani Sankar, D. Kumar, K. Seethalakshmi, Performance Study of Various Adaptive Filter Algorithms for Noise Cancellation in Respiratory Signals, Signal Processing: A International Journal (SPIJ), Volume (4): Issue (5), pp 267-277

[3] Jyoti Dhiman, Shadab Ahmad, Kuldeep Gulia, Comparison between Adaptive filter Algorithms (LMS, NLMS and RLS), International Journal of Science, Engineering and Technology Research (IJSETR), volume 2, issue 5, may 2013, pp 1100-1103.

[4] S. Haykin, Adaptive Filter Theory, Englewood Cliffs, N.J.: Prentice-Hall, Inc.,3rd_{Edition (1996).}

[5] Bart kosko, Neural Network and Fuzzy system, PHI: Prentice-Hall, India, Inc (1992)

[6] B. Widrow and E. Walach, Adaptive Inverse Control, Prentice-Hall, Inc., S.S.Series, N.J. (1996).

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LMS NLMS RLS BPNN

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Algorithm Used

Comparision of MSE of LMS,

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AUTHOR’S PROFILE

Miss Ipsita Dash: She received her B.Tech Degree in Applied Electronics & Instrumentation from Silicon Institute of Technology, Bhubaneswar, BPUT, Odisha. M.Tech scholar of Electronics & Instrumentation with Specialization in VLSI Design & Embedded System from ITER, SOA University, Odisha.