Noise Removal in Cardiac Signal by Shadow Digital Filters

Noise Removal in Cardiac Signal by Shadow Digital Filters

N.Mohana Rao 1, A.S.Srinivasa Rao 2, P.V.Muralidhar 3, Venkata L N Sastry.D 4

1. Aditya Institute of Technology and Management, Tekkali, Srikakulam, AP, India. 2.Professor , Department of Electronics and Communication Engineering, Aditya Institute of

Technology and Management,Tekkali,Srikakulam,AP,India.

1. Associate Professor , Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management,Tekkali,Srikakulam,AP,India.

2. Assistant Professor, Department of Electronics and Instrumentation Engineering, Aditya Institute of Technology and Management,Tekkali,Srikakulam,AP,India.

Abstract

Heart attacks mostly occur in people who suffer from heart or heart-relate diseases, if these diseases are not detected early enough and treated problem will be occurred. There is therefore the need for a reliable means of detecting these diseases to save the patients from these attacks which are increasing in proportion all over the world. Electrocardiograph (ECG), which measures the electrical activity of the heart, generates a signal referred to as ECG signal or simply ECG and the shape of this signal tells much about the condition of the heart of a patient. Naturally the ECG signal gets distorted by

different artifacts which must be removed otherwise it will convey an incorrect information regarding the patients heart condition. One of the ways to eliminate ECG Artifacts is using Digital filters with shadow mechanism. We apply a specific filter which will allow only the desired signal to pass, thus the noise will be removed efficiently. In this proposed work we calculate the signal to noise ratio of ECG signal by different shadow factors of shadow filters.

Keywords: Digital Filters, shadow Mechanism, Electrocardiograph.

1. Introduction

   Digital filter plays an important role in digital signal processing applications. Digital filters are widely used in digital signal processing applications, such as digital signal filtering, noise filtering, signal frequency analysis, speech and audio compression, biomedical signal processing and image enhancement etc.[1]. Traditionally, most digital filter applications have been limited to audio and high-end image processing. With advances in process technologies and digital signal processing methodologies, digital filters are now cost- effective in the IF range and in almost all video markets[2].Finite impulse response (FIR) digital filter impulse response is finite, so it can be used for Fast Fourier Transform (FFT) algorithm to achieve the filtered signal, which can greatly improve the efficiency of operation. In addition, FIR digital filter can be designed a linear phase digital filter which is convenient for image processing and data transmission applications [3]

   FIR Filter banks are used to perform short- term spectram Analysis in a variety of speech processing systems[4]. Many Window functions

   are widely used in digital signal processing for various applications in signal analysis and estimation, digital filter design and speech processing[5]. Heart rate frequency is very important for health status information. The frequency measurement is used in many medical or sport applications like stress tests or life treating situation prediction.[6]. The accurate extraction of the AA signal from the ECG of AF is of great interest for subsequent analysis, since it has been documented to provide significant information on the properties of AF episodes [7].Reduction represents another important objective of ECG signal processing. In fact, the waveforms of interest are sometimes so

   heavily masked by noise that their presence can only be revealed once appropriate signal processing has first been applied.[8] In the last two decades, spectral analysis of the residual ECG signal (rECG, i.e. an ECG signal in which ventricular components were canceled through beat averaging techniques) has been employed to characterize atrial activities[9].VF is raditionally described as a system of many chaotic in the myocardium wandering, electrical wavelets, ever changing in direction and number. In contrast, recent findings indicate that stable organized centers of rapid activity, called mother rotors[10].

2. ECG PREPROCESSING

   Considerable attention has been paid to the design of filters for the purpose of removing baseline wander and power line interference, both types of disturbances imply the design of a narrowband filter. Removal of noise because of muscle activity represents another important

   filtering problem being much more difficult to handle because of the substantial spectral overlap between the ECG and muscle noise. Muscle noise present in the ECG can, however, be reduced. Whenever it is appropriate to employ techniques that benefit from the fact that the ECG is a recurrent signal. For example, ensemble averaging techniques can be successfully applied to time-aligned heartbeats for reduction of muscle noise.

   The filtering techniques are primarily used for preprocessing of the signal and have as such been implemented in a wide variety of systems for ECG analysis. It should be remembered that filtering of the ECG is contextual and should be performed only when the desired information

   remains undistorted. This important insight may be exemplified by filtering for the removal of power line interference. Such

   filtering is suitable in a system for the analysis of heart rate variability.

3. Shadow mechanism

   In shadow filter mechanism the base filter output is feedback either positively or negatively by a shadow filter of same type or different type .Here we used the shadow mechanism to find best combination for different values of b for which the side lobe attenuation is more.

   Distorted ECG signal

   Band reject filter:

   1. Lower cut-off frequency: 59.5 Hz

   2. Upper cut-off frequency: 60.5 Hz

   3. Sampling frequency : 500 Hz The desired transfer function of filter is

      By multiplying the desired transfer function with windows we can get transfer function of FIR band reject filter i.e.

      Ho (z) Noise free

      +/-

      Band Reject Filter

      output signal

      High pass Filter

      High pass Filter

      Where represents is Transfer function of following windows

      Shadow Filter b*(LPF/HPF)

      Shadow Filter b*(LPF/HPF)

      Hd (z)

      Figure: 1 Block diagram for Shadow filter mechanism

      To achieve shadow filter we can use any combinations like band pass in main path and band stop in feedback path.

      Hence we can derive expression of the transfer function for the shadow mechanism with positive feedback connection is,

      Ho (z) =

4. Design of Band Reject Filter

   The Band Reject filter removes the corrupting power line frequency noise in ECG signal. The power line frequency is 60Hz and sampling frequency is 500Hz. The order of the filter is 25. Steps to design

   1. Hamming window

   2. Hanning window

   3. Kaiser window

      1. Design of High Pass Filter

         The high pass filter removes the corrupting low frequency noises in ECG signal. The order of the filter is 25.

         Steps to design High pass filter:

         Lower cut-off frequency: 0.5 Hz The desired transfer function of filter is

         By multiplying the desired transfer function with windows we can get transfer function of FIR band reject filter i.e.

         Where represents is Transfer function of following windows

         1. Hamming window

         2. Hanning window

         3. Kaiser window

      2. Design of Shadow Low Pass filter

         Specifications:

         1. cut-off-frequency:0.2

         2. Filter order:25

            The desired transfer function of filter is

            By multiplying the desired transfer function with windows we can get transfer function of FIR band reject filter i.e.

            Where represents is Transfer function of following windows

            1. Hamming window

            2. Hanning window

            3. Kaiser window

               The Time response and frequency response is shown in below fig: 1

               Fig:1 Frequency response of the Band reject filter

      3. Design of Shadow High Pass filter

         Specifications:

         1. cut-off-frequency:0.2

         2. Filter order:25

            The desired transfer function of filter is

            By multiplying the desired transfer function with windows we can get transfer function of FIR band reject filter i.e.

            Where is represents Transfer function of following windows

            1. Hamming window

            2. Hanning window

            3. Kaiser window

      The Time response and frequency response is shown in below fig:2

      Fig2. : Response of the high pass filter

5. Results and Implementations

   The results shows responses of the cascaded high pass filter and Band reject filter and compares the signal to noise ratio of ECG signal before and after the filtering for different Shadow filters.

   Time domain

   						kaiser w indow

   						kaiser w indow

   2

   1

   0

   Amplitude

   Amplitude

   -1

   -2

   -3

   -4

   -5

   -6

   50 100 150 200 250 300 350

   Samples

   .

   Fig3: Noised ECG signal

   When the raw ECG signal of fig 4 is filtered with the two filters in cascade the whole noises were removed, producing a near clean ECG signal of fig:4 to fig:6 without shadow filters. and form fig:7 to fig:9 shows the same responses with shadow low pass filter with different factors.

   Time domain

   						Hanning w indow

   						Hanning w indow

   2

   1

   0

   Amplitude

   Amplitude

   -1

   -2

   -3

   Fig6:Response of cascaded filters with Kaiser window

   Time domain

   					shaodow lpf+hanning-0.2

   					shaodow lpf+hanning-0.2

   2

   1

   0

   Amplitude

   Amplitude

   -1

   -2

   -3

   -4

   -5

   -6

   50 100 150 200 250 300 350

   Samples

   -4

   -5

   -6

   50 100 150 200 250 300 350

   Samples

   Fig7:Response of cascaded filters with shadow lpf+hanning and factor:0.2

   Fig4:Response of cascaded filters with hanning window

   Time domain

   					shadow lpf+hanning-0.4

   					shadow lpf+hanning-0.4

   2

   1

   0

   Amplitude

   Amplitude

   -1

   -2

   Time domain

   						Hamming w indow

   						Hamming w indow

   2

   1

   0

   Amplitude

   Amplitude

   -1

   -2

   -3

   -4

   -5

   -6

   50 100 150 200 250 300 350

   Samples

   -3

   -4

   -5

   -6

   50 100 150 200 250 300 350

   Samples

   Fig8:Response of cascaded filters with shadow lpf+hanning and factor:0.4

   Fig5: Response of cascaded filters with Hamming window

   Time domain

   					shadow lpf+hanning-0.6

   					shadow lpf+hanning-0.6

   2

   1

   0

   Amplitude

   Amplitude

   -1

   -2

   -3

   -4

   -5

   -6

   50 100 150 200 250 300 350

   Samples

   Fig9:Response of cascaded filters with shadow lpf+hanning and factor:0.6

   Table1: Without shadow Filters

   WINDOW	SNR in dB
   HAMMING	17.3018
   HANNING	17.3021
   KAISER(10.5)	17.3048

   Table2: with shadow LPF

   FEED BACK FACTOR	WINDOW	SNR in dB
   0.2	HAMMING	18.0549
   0.4		18.7800
   0.6		19.4790
   0.2	HANNING	18.0552
   0.4		18.7802
   0.6		19.4793
   0.2	KAISER(10.5)	18.0580
   0.4		18.7831
   0.6		19.4822

6. Conclusion

   In this paper we are using shadow based FIR filters to remove power line interferences 60 Hz of ECG signal .Here we attains different SNR values by varying feedback factor and observing the Table-1 and table-2,it is clearly observed that SNR of clean ECG signal is improved (Table-2) for different feedback factors of shadow filter compared to digital filters without shadow filters(Table-1).

7. References:

1. Saurabh Singh Rajput, Dr.S.S. Bhadauria- IMPLEMENTATION OF FIR FILTER USING EFFICIENT WINDOW FUNCTION AND ITS APPLICATION IN FILTERING A SPEECH SIGNAL-International Journal of Electrical, Electronic and mechanical control.

2. Sonika Gupta, Aman Panghal- Performance Analysis of FIR Filter Design by Using Rectangular, Hanning and Hamming Windows Methods- International Journal of Advanced Research in Computer Science and Software Engineering- Volume 2, Issue 6, June 2012-

ISSN: 2277 128X

[3]. TAO ZHANG- RESEARCH ON DESIGN FIR DIGITAL FILTER USING MATLAB AND WINDOW FUNCTION METHOD-

Journal of Theoretical and Applied Information Technology- 10th February 2013. Vol. 48 No.1 [4].R.W.Schafer, L.R.Rabiner, O.Herrmann- FIR digital filter banks for speech analysis- The Bell system Technical Journal-vol: 54, no:3,march 1979.

[5]. Mridula Malhotra, The Performance Evaluation of Window Functions and Application to FIR Filter Design-International Journal of Scientific & Engineering Research, Volume 2, Issue 12, December-2011 1 ISSN

2229-5518

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4. R. Sassi, L.T. Mainardi, P. Maison- Blanche3 and S. Cerutti-ESTIMATION OF SPECTRAL PARAMETERS OF

   RESIDUAL ECG SIGNAL DURING ATRIAL FIBRILLATION USIG AUTOREGRESSIVE MODELS.

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   ECG during Ventricular Fibrillation a Computer Model Study

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Mohanarao Nawpada obtained bachelor degree under JNTU Hyderabad, pursuing M.Tech Under JNTU Kakinada His areas of interest are Automation of Industrial process, Tuning of Controllers, Signal Processing and Designing of Digital Filters.

Adari Satya Srinivasa Rao received his M Tech. degree from Andhra University, Vishakapatnam in 2004 and he is a PhD student in Andhra University. He is having 15 years of teaching experience in various engineering colleges.

Presently, he is working as Professor, Department of ECE, Aditya Institute of Technology and Management,Tekkali. His interests include signal processing,adaptive antenna arrays and communication systems.

P.V.Muralidhaar obtained M. Tech from JNTU, Hyderabad, pursuing PhD form Berhampur University. He is having an experience more than 10 years and also having more number of both national and international journals, conferences. His area of interest is signal processing,presently working with AITAM , Tekkali, Srikakulam, A.P

D.V.L.N. Sastry, obtained bachelors degree under JNTU Hyderabad. His areas of interest are adaptive PID controllers, fractional PID controllers used in various batch processes and flow control loops and digital PID controllers and design of digital filters

by windows.