A Case Study on Various ECG Denoising Techniques: PhD Thesis Abstract

ECG signal: The ECG signal is an interpretation of heart’s electrical activity. It consists of well-defined successive sets of waves namely P, Q, R, S, T waves. A great intention behind studying ECG signals is for cardiac diagnosis. Its frequency ranges from 0.67Hz to 120 Hz where 0.67Hz is observed in Bradycardia when pulse rate is 40 beats/min P and T waves are low frequency components in the range of 5-9 Hz whereas QRS complex has the higher frequency components.

Two categories of noises are classified:

1. PERSISTENT NOISES: These consist of noises from all the leads having the same temporal distribution but with different intensities. It contains the low frequency range (BASE LINE WANDER), medium frequency (POWER LINE INTERFERENCE) and the high frequency (EMG) signals.

2. BURST NOISE: This is a kind of White Gaussian Noise which appears as subsets on leads for a very short duration of time, eg: electrode popup noise, motion artifacts, electro surgical noise etc.

To remove the persistent noise we can use the following techniques:

A simple zero phase low pass filter with cutoff frequency 140 Hz can be used to remove the EMG noise.

To remove BLW noise a simple Fast Fourier Transform (FFT) can be done or by the process of Empirical Mode Decomposition in which each IMF is passed through a bank of low pass filters and thresholding is done to remove the noisy components. At the end signal is regenerated again to form a noise free signal.

PWI can be removed by using simple FIR notch filters of appropriate cut off frequencies.

To remove the burst noise PCA (Principal Analysis Technique) can be used in which data is decomposed in a set of orthogonal vectors called principal components. The first ones contain the low frequency while the last ones contain the high frequency components like the WGN noise. The WGN noise component is identified and subtracted from the original signal.

Other Different Techniques:

WAVELET DENOISING OF ELECTROCARDIOGRAM:

A major part of real time ECG signals are often corrupted by various noises. Wavelet DE noising method is one such method of noise removal. Discrete wavelet transforms which is a type of wavelet transform which is used to remove White Gaussian Noise.

ALGORITHM USED:

This method is applied to noisy ECG signal corrupted by 0 dB White Gaussian Noise (WGN).The analysis of different levels DWT show that 1st level of DWT detailed sequence contains mainly the WGN noise. The approximation sequence is the superposition of WGN noise and the R peaks. Discarding the R peaks will give the 1st level WGN approximation sequence which in conjugation with the 1st level detailed sequence gives the estimated WGN.

The demising procedure consists of 2 phases:

The pre-processing phase The smoothing phase

The pre-processing phase consists of:

1. The high pass filtering of noisy ECG signal with cut off frequency as 0.18Hz to remove the DC offset present at the original noise free signal.2. Determining the best mother wavelet function which in this case is chosen to be ‘symlet8’ based on the reduced mean square error value obtained.

The smoothing phase consists of:

1. To apply one level DWT to noisy ECG signal and to estimate the WGN signal.2. To apply 4 level decomposition of noisy ECG signal and to evaluate detailed sequences (Cd1, Cd2, Cd3, Cd4) and approximation coefficients (Ca4).3. To apply 4th level DWT to estimate WGN and to evaluate the Cd4 detailed sequence (Cdn4).4. To estimate the 4th level detailed sequence of noise free ECG signal Cdf4 = Cd4 – Cdn4.5. To compute the used demising threshold given by T = (2*(log)) * ½ *median (abs (Cd1))/0.6745 where N is the length of the signal.6. To threshold the detailed sequences using computed thresholds each for Cd1, Cd2, and Cd3 to obtain Cdt1, Cdt2, and Cdt3 respectively.7. To reconstruct the demised ECG signal by IDWT using detailed sequences Cdt1, Cdt2, Cdt3 and Cdf4 and approximation sequence Ca4.

This is a smoothing based algorithm on wavelet applied on ECG signal. The White Gaussian Noise can be easily detected and removed by it.

HILBERT VIBRATION DECOMPOSITION: It is used to remove the Baseline wander noise. It decomposes the data into highest amplitude components coming first followed by the lower amplitude components. The BLW comprises the highest energy component in a noisy ECG signal and hence can be easily detected. The HVD identifies this largest energy component as the average function of instantaneous frequency composition called as IF. Then the extraction of the corresponding envelope is done which is then subtracted from the original signal to generate a noise free signal. By using HVDT the original signal x (t) can be expressed as:

EMPIRICAL MODE DECOMPOSITION: It is a method which does not require any pre or post processing. It is a method of breaking down the signal without leaving the time domain. This process generates its basis function based on the input signal only.

ALGORITHM USED: It filters out functions which form a complete and nearly orthogonal basis for the original signal. Completeness is based on the methods of EMD. Noisy data is decomposed into oscillatory components called Intrinsic Mode Function(IMFs).It then involves the thresholding of each IMF to discard the noise generating components and then reconstruct to generate the noise free signal.

DONHO’S DENOISING TECHNIQUE: This involves DWT of the signal to a certain level and then evaluating the value of threshold. The signal is then passed through the threshold calculated to discard certain components coming below this value and then taking IDWT to generate a noise free signal. The value for threshold is given by

T = a .√2 log(n)

Where a=noise standard deviation; n = number of samples, T = threshold

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