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24 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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يملاسا دازآ هاگشناد

دهشم دحاو

یسدنهم و ینف هدکشناد

هورگ
یکشزپ یسدنهم



هتسسگ یاهلانگیس شزادرپ



هرامش شرازگ
1
:

wavelet

اب طبترم یاه هلاقم


:هدننک هیهت

یئامس داشرف


Farshad.Samaei@gmail.com

:سرد داتسا

عس رتکد
ی
د

تحار
ی


1
_
ECG Signal Denoising By Wavelet Transform Thresholding


American Journal of Applied Sciences 5 (3): 276
-
281, 2008


ISSN 1546
-
9239

© 2008 Science Publications

Authors:

Mikhled Alfaouri and Khaled Daqrouq

Question
:

comparing

presented

Method

in this paper
with the Donoho's method for signal
denoising
.


M
ethod
:

The presented method based on decomposing the signal into five levels of wavelet
transform by using Daubechies

wavelet (db4) and determining a threshold through a loop to find
the value where minimum error is achieved between the detailed coefficients of
thresholded
noisy signal and the original. The method can be divided into the following

steps:

1
-
Noise Generati
on and addition

2
-

Decomposing of the noisy and original signals using wavelet transform

3
-

Choosing and applying threshold value

4
-

Reconstruction


Mat
e
rial
: Four different signals are used to study the effect of threshold value of discrete
wavelet transf
orm coefficients. These signals are considered as original and free of noise ECG
signal with different morphology.


R
esults
:

The wavelet transform allows processing nonstationary signals such as ECG signal.

presented method is superior

than

Donoho’s



method and better PDS and SNR obtained.


_________________________________________



2
-

ECG De
-
Noising using

improved thresholding based on
Wavelet

transforms

IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.9,
September 2009

Authors

:

G. Umamaheswara Reddy, Prof. M. Muralidhar, Dr. S. Varadarajan


Question
:

comparing improved thresholding method by soft and hard thresholding methods.


M
ethod
:

The method can be divided into the following steps:

1
-
Noise Generation and addition

2
-
Decomposing of the noisy signals using wavelet Transform.

3
-
Apply thresholding, to obtain the estimated wavelet coefficients dj

4
-
Reconstructing the de
-
noised ECG signal form


approximations and details

coefficients by
inver
se

wavelet transform.


Evalua
tion Criteria:

We utilize both the mean square error (MSE) value and SNRo value
between the constructed

de
-
noised

ECG

signal and the original ECG signal.

Mat
e
rial

:

ECG signal in MIT
-
BIH database is intercepted

to be the original ECG signal. The
length of the original ECG signal (i.e., the number of the sample points) is N=1024
.


R
esults
:

This new de
-
noising method could be regarded as a compromising between hard
-

and
soft
-
thresholding de
-
noising methods. The experimental results indicate that the

proposed
method is better than traditional wavelet thresholding de
-
noising methods in the aspects of
remaining geometrical characteristics of ECG signal and in improvement of signal
-
to
-
noise ratio
(SNR).

____________________________________



3
-

Optimal
selection of wavelet basis function applied to

ECG
signal denoising


Digital Signal Processing 16 (2006) 275

287

Authors

:
Brij N. Singh,
&

, Arvind K. Tiwari


Question
: selecting best mother wavelet basis functions for denoising

of the ECG signal in
wavelet domain and is it retaining the signal peaks close to their full amplitude?


Answer
: The obtained wavelet based denoised ECG signals retain the necessary diagnostics
information contained in the original ECG signal.


M
ethod
:

Th
us following steps will lead determination of optimal wavelet applied to the ECG
signal:

1. Select the basis wavelet filter, low pass, decomposition from wavelet filter bank library.

2. Compute the cross correlation coefficient between ECG signal and selec
ted wavelet filter.

3. Select the optimum wavelet filter which maximizes the cross correlation coefficient.

The data (xi, yi ) were generated from a model of the form yi = f (xi ) +
ε
i , {
ε
i } i.i.d.

N(0,
σ
2), where {xi } are equispaced in [0, 1], x0 = 0 an
d xn = 1. The factors are:

(a) ECG signal sample sizes n.

(b) Values of
σ
2.

(c) The thresholding methods.

(d) Type of Mother Wavelet basis function:

Daubechies filter (Db) of order 4, 6, 8, 10, 12;

Symmlet filter (Sym) of order 4, 5, 6, 7, 8;

Coiflet filte
r (C) of order 1, 2, 3, 4, 5;

Battle

Lemarie (Bt) filter of order 1, 3, 5;

Beylkin filter (Bl);

Vaidyanathan filter (Vd).

For each combination of these factors, a simulation run was repeated 50 times keeping all factor
levels constant, except the {
ε
i } tha
t were regenerated. In order to compare the behavior of the
estimation methods performance criteria viz. root mean square error (RMSE), root means square
bias (RMSB), and L1 norm are employed. The computational platform selected is Intel Pentium
(III), 500

MHz processor, 128 MB RAM. All simulations are performed in MATLAB.


Mat
e
rial
:

In present investigation, the ECG signal was taken up from biological signal
processing (BSP) demonstration data sheet (400 s @ 500 Hz).


R
esults
:

The experimental results have revealed suitability of Daubechies mother wavelet of
order 8 to be the most appropriate wavelet basis function for the denoising application. The
selected basis function has been found to be optimal not only in terms of root

mean square errors
(RMSE), but also it preserves the peaks of the ECG signal, which contains valuable
physiological information for diagnostic purpose.

________________________________________



4
-

MULTIRESOLUTION ANALYSIS: THE DISCRETE

WAVELET TRANSFORM

T
HE
W
AVELET
T
UTORIAL

P
ART
IV

By:

Robi Polikar

136 Rowan Hall

Dept. of Electrical and Computer Engineering

Rowan University

Question
:

Author

in this paper introduces the Discrete wavelet transform and t
he foundations of

it.

Why is the Discrete Wavelet T
ransform Needed?

Answer
:

The discrete wavelet transform (DWT), provides sufficient information both for analysis and
synthesis of the original signal, with a significant

reduction in the computation time

.The DWT is
considerably easier to implement when
compared to the CWT.


__________________________________________



5
-

A new adaptive scheme for ECG enhancement


Signal Processing 75 (1999) 253
}
263

Author
:

V. Almenar, A. Albiol


Question
:

In this paper author
reviewed previously published event
-
related adaptive
filters(AICF
&
TSAF) and then
presented his method(SIF)

then

compares

them in term of
distortion and noise
reduction.


Method:

author

present a new adaptive filtering scheme. that is

a combination of the two previous
algorithms: the TSAF and the AICF. he has Generated two independent records of white Gaussian noise.

The noisy ECG signals have been obtained by adding each ECG signal to a di
ff
erent noise record.

In order to vary the sig
nal to noise ratio (SNR) noise records are multiplied by a constant gain factor
before the addition.

Comparing output
x
o
[
n
] with the original clean ECG input
x
i
[
n
],

we can measure the
distortion
D
introduced by the SIF
fi
ltering. Distortion is de
fi
ned as:



And
the noise reduction NR is a comparison between output power and input power


Mat
e
rial
:

The signals used in these measurements have been obtained from the MIT
-
BIH database.
They are the clean ECG signals from annotations 118 and 119 (they consist of
two clean signals from
di
ff
erent leads, and they have a duration of 30 min).


R
esults
:

The SIF do a better job than the TSAF (and the TSAF is better than the AICF) in keeping the

Individual shape of each beat. and it provides greater noise reduction.