Biomedical Engineering Reference
In-Depth Information
The graythresh function uses Otsu's method, which chooses the threshold to
minimize the intraclass variance of the black and white pixels.
Multidimensional arrays are converted automatically in to 2D arrays using
reshape. The graythresh function ignores any nonzero imaginary part of I.
[level EM] = graythresh (I) returns the effectiveness metric, EM, as the second
output argument. The effectiveness metric is a value in the range [0 1] that indi-
cates the effectiveness of the thresholding of the input image. The lower bound is
attainable only by images having a single gray level, and the upper bound is
attainable only by two-valued images.
Proposed Method
In this chapter we have taken the pure EEG signals having four samples that are
then mixed with noise. The signal is processed with ICA and then further with
wavelet denoise. ICA is applied so as to separate the signals from a multichannel
source of signals and then wavelet denoising to remove noise from an independent
component of the signal; we find that the final signal shows better artifacts removal
as compared to simple filtering methods. The complete process is explained in the
following algorithm:
Algorithm
1. Plot the EEG signal that is mixed with noise with respect to j and k.
2. Apply conventional filtering through Kurtosis that is defined by Eq. (
5
):
Kurt
ðÞ¼
Ey
4
3E
f
y
2
g
2
3. Let the original signal be defined by X, then basic ICA model is expressed by
Eq. (
11
)
X
¼
AS
þ
N
ð
11
Þ
where,
A
mixing matrix
S
independent component
N
noise added
4. To denoise the image using ICA we have to process the image. The prepro-
cessing consists of two steps:
a. Centering: the signal is first centered, i.e., we substract the image mean from the
noisy image. It is expressed mathematically as in Eq. (
12
):
X
X
E
fg
ð
12
Þ
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