صفحه 1:
Oniversity:
Oduptive-Piterioy-Oused انسی9) Por Ieppusive Ovise
Cucwvelativd Prow CCG Gicperal
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Suppression of noise and artifacts is a necessary step in biomedical
data processing. Adaptive filtering is known as a useful method to
overcome this problem. Among various contaminants, there are some
situations such as electrical activities of muscles contribute to
impulsive noise. This paper deals with modeling real-life muscle noise
with a-stable probability distribution and adaptive filtering noise
cancellation assessment with maximum correntropy criterion (MCC) as
adaptive technique. Based on our test on some data of MIT-BIH
arrhythmia and EMBC databases, we achieve an improved signal to
ratio (SNR) in any electrocardiogram (ECG) signal corrupted by
impulsive noise
صفحه 3:
The worst achieved improvement based on setting the
best parameter values using trial and error for both filter
and utilized algorithm is 9.5 dB with correlation coefficient
value of 0.93.
The SNR improvement on the whole utilized database
records is 11.03 dB on average. The proposed algorithm is
applied to the records from MIT-BIH arrhythmia and EMBC
databases to remove the impulsive noise. A computer
simulation is used to create and add it to the ECG signals.
5 Simulation results are also provided to support the
Oy .discussions
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صفحه 4:
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صفحه 5:
Due to significant technological advances in signal
processing, system enhancements of biomedical signal
analysis has become a major research field. Among the
biomedical signals, electrocardiogram (ECG) acquires
the most studied type for decades. Work on ECG signals
has become a significant tool to diagnose for cardiac
disorders [1]. Each segment of this biomedical signal
type carries various types of important information for
the clinician analyzing the patients’ heart condition. For
instance, the amplitude and occurrence of the P wave,
and the duration of the Q wave, R wave and S wave
(QRS) morphography are indicative of the cardiac
uscles mass condition [2]. Loss of amplitude indicates
uscle damage whereas increased amplitude indicates
abnormal heart rates [3]. ..
عم
صفحه 6:
IDPOLGIOE 02۵۰1۵0۶ 0۵0
According to the central limit theorem, Gaussian
distribution could be considered for all real applications,
when enough samples from a distribution are available.
This issue is underlined in the signal processing field
where Gaussian distribution is utilized to model the
random noise in a signal [24]. However, a wide variety of
signals found in practice arise non-Gaussian impulsive
behavior [25]. This impulsive phenomenon exhibits in
high peaks in small time durations
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(,غ)م
صفحه 7:
Atmospheric radio noise, telephone lines noise, office
equipments noise, and multi-user interference in
mobile communication systems are some typical
examples of impulsive noise. Furthermore, in
biomedical engineering, while using surgical device,
and in electrocardiology, i.e. ......
صفحه 8:
OBTO DEGORIPTMODO
The proposed method is tested on ECG data obtained
from MIT-BIH arrhythmia database [34]. These
recordings were obtained from inpatients and
outpatients intended to serve as a representative
sample of the variety of waveforms and artifact that
an arrhythmia detector might encounter in routine
clinical use. Segments selected in this way were
excluded only if neither of two ECG signals was of
Dy سا quality for analysis by human experts
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صفحه 9:
. Two-channel ambulatory ECG recordings were
obtained by placing the electrodes on the chest.
The upper signal is modified limb lead II (MLII)
and the lower is usually a modified V1 (both
electrodes are placed on the chest). The
recordings were digitized at 360 samples per
second per channel with 11-bit resolution over a
10 mV (+5 mV)......
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GSIDDLOMOD REGOLNSE BOD OIGCOGE1006
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In order to assess our MCC-based impulsive denoising technique
performance, we have achieved some simulated scenarios on some
the segments from the mentioned databases. The selected
segments contain impulsive noise by adding simulated impulse
noise. A segment of the record 101 from MIT-BIH arrhythmia
database and another of the record D102 from second database are
used to demonstrate the performance of the proposed method
a = Proposed
stam
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COOCLOG10OG
This paper investigated the performance of the maximum
correntropy criterion (MCC) based algorithm in suppression
of impulsive noise types from ECG signals. Results indicated
that using MCC-based algorithm as the adaptive algorithm
with noise cancellation setup has a noticeable effect on
denoising ECG signals. Moreover, the implementation of
proposed algorithm was successfully accomplished, with
results that have an extremely positive and favorable
response.
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It is evident from the results that the selection of suitable
parameter values, such as filter length, step size, and kernel
width, results in a positive effect on performance of the adaptive
filter and the utilized adaptive algorithm. Based on our test on
some data of MIT-BIH arrhythmia and EMBC databases, we
achieve an improved SNR in any ECG signal corrupted by
impulsive noise.
The worst achieved improvement based on setting the best
parameter values
using trial and error for both filter and utilized algorithm was
9.5582 dB with
correlation coefficient value of 0.9390.
R improvement on the whole utilized database records
(Table Il) was
11.0331 dB on average
مه
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The End
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