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Method to reduce the effect of miagrafic and sensory noise with isolating the isoline on ECG signal

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MATEC Web of Conferences 132, 05017 (2017) DOI: 10.1051/matecconf/201713205017
DTS-2017
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution
License 4.0 (http://creativecommons.org/licenses/by/4.0/).
*
Corresponding author: sea.sea@mail.ru
Method to reduce the effect of miagrafic and sensory noise with
isolating the isoline on ECG signal
Evgeny Semenishchev
1,*
, Dmitry Chernyshov
1
, Ilya Svirin
2
1
Don state Technical University, 344000, Rostov-on-Don, Russian Federation
2
CJSC Nordavind, 117545, Moscow, Russian Federation
Abstract: This paper deals with an approach to the analysis of ECG data, which allows to remove the
noise component, to preserve the peaks characterizing the work of the heart, and to identificate floating
isoline.
1 Introduction
Processes automation is one of the priorities of the
modern world. Constant modernization of equipment
requires large investments, as well as retraining of
personnel, installation works and maintenance of new
systems. In this connection, the most promising direction
is the automation of data processing, which allows to
reduce the number of personnel, and to improve the
overall efficiency of the control system. One of the
leading areas of data analysis is the development of the
digital signals processing theory, based on the analysis
of initial data.
The use of digital signal processing methods has
found wide application [1]:
- in automation and control systems
- in modern antenna systems
- in the study of biomechanical parameters, biometric
data collection systems located directly on the object
under study
- in modern systems of computer vision and
automatic processing of two-dimensional signals
- in economics and sociology in the study of trends;
- in information-measuring systems;
- in computer technology to increase accuracy of
analog-digital transformation
Data, received with ECG, in addition to the useful
information on the propagation of electrical impulses in
the cardiac muscle, contains the noise component, which
has a different source. Analog-digital convertion also
adds random distortions to signal [2].
The main task in the signal processing is the
separation of the useful component and attenuation of
the noise component. In practice, a the mean-square
error minimization criterion or the criterion of mean-
absolute deviation is used to determine the processing
quality. Each of this methods has advantages and
limitations of use depending on the task and a priori
information on the components of the input signal. In
this connection, digital signal processing based on the
objective function of the combined criteria becomes
relevant objective. Of particular interest is the use of
multicriterial methods for processing digital signals, that
are represented by a single implementation with a
limited amount of a priori information about the useful
component function and statistical noise characteristics
[3].
Figure 1 contains an algorithm for obtaining
estimates using multi-criteria signal smoothing methods,
based on minimizing the objective functions:
2
1
2
21
1
2
21
2)(),...,,(
n
k
kkk
n
k
kkn
sssYssss
when
– adjusting factor.
When implementing the considered smoothing
methods using machine modeling, in the form of
programs, regions of values of the coefficients were
obtained
44
.
4
01
,
0
. ECG data processing requires
real time analysis. It is done by finding estimates of the
objective function in window
k
, and applying sliding
window
l
for all values of the input signal. Here
k
is
size of the window and
is step of sliding window.
The process of obtaining estimates in a sliding
window is performed by parallel processing of the initial
values of the multicriteria objective function in the
window
k
, with various processing parameters
. The
transition between estimates obtained with different
parameters
is accomplished by the condition:
2
1 12
2
2 12
() () ()
() () ()
k kk
k
k kk
s ss p
s
s ss p




Here
)(
1
k
s
,
)(
2
k
s
are input realization
estimates, received with parameters