FIR Filter Design Analysis for Power Line Interference in ECG Signals

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 1 | Issue 6 | November 2014 ISSN (online): 2349-6010

FIR Filter Design Analysis For Power Line Interference In ECG Signals Pankaj Srivastava ME Student ECE National Institute of Technical Teachers Training and Research, Chandigarh, India

Rajesh Mehra Associate Professor ECE National Institute of Technical Teachers Training and Research, Chandigarh, India

Abstract Pick up of hum from power line is a very common phenomenon in the measurement of ECG signals. It is of prime need to reduce the variations coming due to power line so that one can analyse the most of the critical points in the measured signal.

Power Line Interference (PLI) may seriously degrade the ECG signal, rendering the ECG analysis inaccurate. In this paper power line interference in the electrocardiogram (ECG) measurement is reduced with the help of FIR filters. FIR filter is designed & simulated to reduce the power line interference in ECG measurement. Developed Filter has been designed and analysed using two Window techniques namely Blackman and Kaiser techniques. Both Window based Filters are designed and simulated using MATLAB. It can be observed that from the simulated result that Blackman based design is better in terms of reduced noise performance, as compared to Kaiser Window based design.

Keywords: ECG, FIR, Kaiser, Blackman and PLI. _______________________________________________________________________________________________________

I. INTRODUCTION Digital Signal processing is concerned with the digital representation of the signal and the use of the digital processor to analyze, modify or extract information from signal. To remove noise and interference from the signal and to obtain the spectrum are the reason for processing a digital signal. Flexibility, no drift in performance with temperature, superior performance is the some advantages of digital signal processing. The major DSP operations are Convolution, Correlation, filtering, transformation and modulation. Digital filters are very important part of DSP. Filters have two uses: signal separation and signal restoration. Signal separation is needed when a signal has been contaminated with interference, noise, or other signals. Signal restoration is used when a signal has been distorted in some way. The two major type of digital filter are FIR and IIR filter. A Finite Impulse Response (FIR) digital filter is one whose impulse response is of finite duration. The general difference equation for a FIR digital filter is ( ) ∑ ( ) (1) where y(n) is the filter output at discrete time instance n, b k is the k-th feed forward tap, or filter coefficient, and x(n-k) is the filter input delayed by k samples. The Σ denotes summation from k = 0 to k = M -1 where M is the number of feed forward taps in the FIR filter. FIR filters are simple to design and they are guaranteed to be bounded input-bounded output (BIBO) stable.FIR filters can have an exactly linear phase response. The impulse response of an IIR filter is of infinite duration. The general difference equation for an IIR digital filter is ( ) ∑ ( ) ∑ ( ) (2) Where ak and bk are the coefficient of the filter, i.e the current output sample, y(n), is a function of past outputs as well as present and past input sample, means IIR is feedback type system. IIR filters are useful for high-speed designs because they typically require a lower number of multiplies compared to FIR filters. IIR filters can also be designed to have a frequency response that is a discrete version of the frequency response of an analog filter.

Another important feature of DSP is to extract power estimation of the signal. Its major use is in the astronomy and biomedical areas. Power density spectrum estimation methods can be divided into two main groups—nonparametric and parametric—where the Periodogram and window methods belong to the former. Windows may be fixed, e.g., the Hanning window; others have parameters for adjusting the side-lobe attenuation, e.g., the Kaiser window. The window methods decrease the variance of the spectrum estimate by smoothing.

II. THE WINDOW TECHNIQUE When the desired frequency response Hd(ejω) of the system has abrupt transitions (as in the case of an ideal low pass filter), then the impulse response hd(n) has infinite duration. The most obvious way to approximate such a filter (system) is to truncate its impulse response to, say, M +1 sample. The impulse response of the new filter (assuming hd(n) is casual) is thus: The last equation can also be rewritten as

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FIR Filter Design Analysis For Power Line Interference In ECG Signals (IJIRST/ Volume 1 / Issue 6 / 034)

h[n]=hd[n] wR[n] (3) where Wr is a rectangular windowing function. It becomes apparent that one can use different windowing functions to truncate/shape the desired impulse response to a finite duration. Let w [n] represents in general a windowing function of length M+1 samples. The truncated impulse response is h[n]=hd[n] w[n] (4) An advantage with Blackman window over other windows is that it has better stop band attenuation and with less pass band ripple. The Blackman window function are expressed as: w(n)=0.42+0.5cos(2 n/N-1)+0.08cos(4 n/N-1) (5) In design of a low pass filter using Kaiser windowing technique, four parameters the passband edge ω p, the stopband edge ωs, passband ripple Ap and stopband attenuation As, are required. The prototype filter h(n) is designed using widely used Kaiser window. Kaiser window function is given by as w(n)=I0( {1-[2n/(N-1)]2}1/2)/ I0( ) (8)

III. ECG SIGNAL

Fig. 1: Typical waveform of ECG signal.

Signal processing, in general, has a rich history, and its importance is evident in such a diverse fields as biomedical engineering, acoustics, Sonar, radar, Seismology, speech communication, data communication, nuclear science, and many others.

In many applications, for example, in EEG and ECG analysis or speech processing it can be used to extract some characteristic parameters. Alternatively, to remove interference, such as noise, from the signal or to modify the signal to present it in a form this is more easily interpreted by an expert. The field of biomedical signal analysis or processing has advanced to the stage of practical application of signal processing and pattern analysis techniques for efficient and improved noninvasive diagnosis, online monitoring of critical patients, and rehabilitation and sensory aids for the handicapped. Techniques developed by engineers are gaining wider acceptance by practicing clinicians, and the role of engineering in diagnosis and treatment is gaining much–observed respect. The major strength in the application of computers in biomedical signal analysis lies in the potential use of signal processing and modeling technique for the quantitative or the objective analysis. Analysis of signals by human observers is almost always accompanied by perceptual limitations, inter-personal variations, errors caused by fatigue, errors caused by the very low rate of incidence of certain sign of abnormality, environmental distortions, and so on.

Electrocardiogram (ECG) is an important clinical tool for investigating the activities of heart, which is one of the signals of vitality. Interpretation of these details allows diagnosis of a wide range of heart conditions. These conditions can vary from minor to life threatening. A typical ECG tracing of a normal heartbeat (or cardiac cycle) consists of a P wave, a QRS complex and a T wave. Figure 1 shows the typical ECG trace.

The electrical activity of the heart is generally sensed by monitoring electrodes placed on the skin surface. The electrical signal is very small (normally 0.0001 to 0.003 volt). These signals are within the frequency range of 0.05 to 100 Hertz (Hz.) or cycles per second. Unfortunately, other artifactual signals of similar frequency and often larger amplitude reach the skin surface and mix with the ECG signals. Artifactual signals arise from several internal and external sources. Means Electro-cardio-graphic signals (ECG) may be corrupted by various kinds of noise. In this paper type of noise we consider is Power Line Interference (PLI) The amplitude, or voltage of the recorded electrical signal is expressed on an ECG in the vertical dimension and is measured in millivolts (mV). On standard ECG paper 1mV is represented by a deflection of 10 mm. An increase in the amount of muscle mass, such as with left ventricular hypertrophy (LVH), usually results in a larger electrical depolarization signal, and so a larger amplitude of vertical deflection on the ECG. An essential feature of the ECG is that the electrical activity of the heart is shown as it varies with time. In other words we can think of the ECG as a graph, plotting electrical activity on the vertical axis against time on the horizontal axis. Standard ECG paper moves at 25 mm per second during real-time recording. This means that when looking at the printed ECG a distance of 25mm along the horizontal axis represents 1 second in time.

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FIR Filter Design Analysis For Power Line Interference In ECG Signals (IJIRST/ Volume 1 / Issue 6 / 034)

ECG paper is marked with a grid of small and large squares. Each small square represents 40 milliseconds (ms) in time along the horizontal axis and each larger square contains 5 small squares, thus representing 200 ms. Standard paper speeds and square markings allow easy measurement of cardiac timing intervals. This enables calculation of heart rates and identification of abnormal electrical conduction within the heart (Figure 2). Electrocardiogram (ECG) compression has been the object of numerous research works. Their main objective is to reduce the amount of digitized ECG data as much as possible with a reasonable implementation complexity while maintaining a clinically acceptable signal. Consequently, reduction of digitized ECG data allows improvement of storage capacity in the memory and/or reduces the cost of transmission.

Fig. 2: sample of ECG strip

IV. RESULTS AND SIMULATION ECG signal is generated by MATLAB Code and is corrupted by the Power Line Interference noise as shown in Fig. 3

Fig. 3: ECG signal corrupted by NOISE

The corrupted ECG Signal is passed through FIR filter using the Blackman Window Technique and output is shown in Fig. 4

Fig. 4: Blackman filter output for Noisy ECG Signal

The corrupted ECG Signal is also passed through FIRfilter using the Kaiser Window Technique and output is shown in Fig. 5.

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FIR Filter Design Analysis For Power Line Interference In ECG Signals (IJIRST/ Volume 1 / Issue 6 / 034)

Fig. 5: Kaiser filter output for Noisy ECG Signal V.

CONCLUSION

Fig. 6: Comparison of filter output for Noisy ECG Signal

Fig 3 shows the corrupted ECG signal by noise. This corrupted signal is passed through Blackman and Kaiser filter and simulated output is shown in fig 4 and fig 5 respectively. These two outputs are compared in fig 6 and we find that output of filter using Blackman Window technique is better in performance as compared to Kaiser window technique

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