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04-08-2014, 02:21 PM
SUPPRESSION OF POWER LINE INTERFERENCE CORRECTION OF
BASELINE WANDERS AND DENOISING ECG SIGNAL BASED ON
CONSTRAINED STABLITY LEAST MEAN SQAURE ALGORITHM
[attachment=66564]
ABSTRACT
The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases.
Good quality ECG is utilized by physicians for interpretation and identification of
physiological and pathological phenomena. However, in real situations, ECG recordings
are often corrupted by Power Line Interference (PLI) and baseline wanders (BW). These
two parameters are called artifacts. Two dominant artifacts present in ECG recordings are:
(1) high-frequency noise caused by electrocardiogram induced noise, power line
interferences, or mechanical forces acting on the electrodes; (2) Baseline Wander (BW)
that may be due to respiration or the motion of the patients or the instruments. These
artifacts severely limit the utility of recorded ECGs and thus need to be removed for
better clinical evaluation. Several methods have been developed for ECG enhancement.
In this paper, we propose a new ECG enhancement method based on the recently
developed Constrained Stability Least Mean Square Algorithm (CSLMS). The
simulations show that the proposed CSLMS method provides very good results for
denoising and BW and PLI removal.
INTRODUCTION
Adaptive digital filters are successfully used in many practical systems such as echo,
noise canceling, line enhancers, speech coding, and equalizers etc. Adaptive filter is a
primary method to filter ECG signal, because it does not need the signal statistical
INTERNATIONAL JOURNAL OF ELECTRONICS AND
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 3, May – June, 2013, pp. 185-192
© IAEME: www.iaemeijecet.asp
Journal Impact Factor (2013): 5.8896 (Calculated by GISI)
www.jifactor.com
IJECET
© I A E M E International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
186
characteristics it operates by adjusting its coefficients in response to its input so as to
effectively process that input. Thus the filter coefficients are a function f the actual data they
process. Another primary advantage of adaptive filters is that they can be used in time
invariant environments. Since an adaptive filter is continuously self designing, it can adapt to
statistical changes in the data [1] This paper we present a novel adaptive filter for removing
the s two types of noise that corrupted the ECG signal which are high frequency noise and
low frequency noise and artifact from the time variant and time invariant ECG signal based
on Constrained Stability Least Mean Square (CSLMS) algorithm. This algorithm is derived
based on the minimization of the square Euclidean norm of the difference weight vector
under a stability constrained defined over the posteriori estimation error. The adaptive filter
essentially minimizes the mean-squared error between a primary input, which is the noisy
ECG, and a reference input, which is either noise that is correlated in some way with the
noise in the primary input or a signal that is correlated only with the. The results show that
the performance of the CSLMS based algorithm is superior to that of the LMS based
algorithm in noise reduction .Baseline Wander and power line interference reduction is the
first step in all electrocardiographic (ECG) Signal processing. The baseline wander is caused
by varying electrode- skin impedance, patient’s movements and breath. The kind of
disturbances is especially present in exercise electrocardiography, as well as during
ambulatory and holter monitoring. The ECG signal is also degraded by additive 50 or 60 Hz
power line (AC) interference. These two artifacts are the dominant artifacts and strongly
affect the ST segment, degrade the signal quality, frequency resolution, produces large
amplitude signals in ECG that can resemble PQRST waveforms and masks tiny features that
are important for clinical monitoring and diagnosis. ECG in the primary input. Finally, we
have applied this algorithm on ECG Signals from the MIT-BIH data base and compared its
performance with the conventional LMS algorithm The goal of ECG Signal enhancement is
to separate the valid signal components from the undesired artifacts, so as to present an ECG
that facilitates easy and accurate interpretation. Many approaches have been reported in the
literature of address ECG enhancement using both adaptive and non-adaptive [1]-[6],
adaptive filtering techniques permit to the detect time varying potentials and to track the
dynamic variations of the signals. In [2], proposed and LMS based adaptive recurrent filter
to acquire the impulse response of normal QRS complexes and then applied it for arrhythmia
detection in ambulatory ECG recordings part from these several adaptive signal processing
techniques are also published, e.g. NLMS algorithm with decreasing step size, which
converge to the global minimum [3], a variable step size NLMS algorithm with faster
convergence rate [4],Costa et al. in [4] proposed a noise resilient variable step size, LMS
which is specially indicated for biomedical application, also several modification are
presented in literature to improve the performance of the LMS algorithm [5]- [8], recently in
[9] presented several less computational complex adaptive algorithms in time domain but
these algorithms exhibits slower convergence rate. The considered CSLMS algorithm is
based o the concept of difference quantities and the constraint of equilibrium in the sequence
of a posteriori estimation errors [10]. The method which applies nonlinearities to the error
and input signal sequences, which can be derived using the Lagrange multiplier method as a
generalization of the normalized LMS (NLMS) under certain conditions the adaptive noise
cancellers (ANC) based on the CSLMS algorithm shows improved performance by
decreasing the excess mean squared error and maladjustment compared to conventional
algorithms like, LMS and NLMS algorithms.
ICONCLUSION
In this paper the process of noise removal from ECG signal using CSLMS based The proposed treatment exploits the modifications in the weight update formula and thus
pushes up the speed over the respective LMS based realizations. Out simulation, however,
confirm that the performance of the CSLMS is better than the LMS algorithm in terms of
SNRI, MSE and maladjustment, this is shown in tables I and II. Hence CSLMS base adaptive
noise canceller may be used in all practical applications.
adaptive filtering is presented.
BASELINE WANDERS AND DENOISING ECG SIGNAL BASED ON
CONSTRAINED STABLITY LEAST MEAN SQAURE ALGORITHM
[attachment=66564]
ABSTRACT
The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases.
Good quality ECG is utilized by physicians for interpretation and identification of
physiological and pathological phenomena. However, in real situations, ECG recordings
are often corrupted by Power Line Interference (PLI) and baseline wanders (BW). These
two parameters are called artifacts. Two dominant artifacts present in ECG recordings are:
(1) high-frequency noise caused by electrocardiogram induced noise, power line
interferences, or mechanical forces acting on the electrodes; (2) Baseline Wander (BW)
that may be due to respiration or the motion of the patients or the instruments. These
artifacts severely limit the utility of recorded ECGs and thus need to be removed for
better clinical evaluation. Several methods have been developed for ECG enhancement.
In this paper, we propose a new ECG enhancement method based on the recently
developed Constrained Stability Least Mean Square Algorithm (CSLMS). The
simulations show that the proposed CSLMS method provides very good results for
denoising and BW and PLI removal.
INTRODUCTION
Adaptive digital filters are successfully used in many practical systems such as echo,
noise canceling, line enhancers, speech coding, and equalizers etc. Adaptive filter is a
primary method to filter ECG signal, because it does not need the signal statistical
INTERNATIONAL JOURNAL OF ELECTRONICS AND
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 3, May – June, 2013, pp. 185-192
© IAEME: www.iaemeijecet.asp
Journal Impact Factor (2013): 5.8896 (Calculated by GISI)
www.jifactor.com
IJECET
© I A E M E International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
186
characteristics it operates by adjusting its coefficients in response to its input so as to
effectively process that input. Thus the filter coefficients are a function f the actual data they
process. Another primary advantage of adaptive filters is that they can be used in time
invariant environments. Since an adaptive filter is continuously self designing, it can adapt to
statistical changes in the data [1] This paper we present a novel adaptive filter for removing
the s two types of noise that corrupted the ECG signal which are high frequency noise and
low frequency noise and artifact from the time variant and time invariant ECG signal based
on Constrained Stability Least Mean Square (CSLMS) algorithm. This algorithm is derived
based on the minimization of the square Euclidean norm of the difference weight vector
under a stability constrained defined over the posteriori estimation error. The adaptive filter
essentially minimizes the mean-squared error between a primary input, which is the noisy
ECG, and a reference input, which is either noise that is correlated in some way with the
noise in the primary input or a signal that is correlated only with the. The results show that
the performance of the CSLMS based algorithm is superior to that of the LMS based
algorithm in noise reduction .Baseline Wander and power line interference reduction is the
first step in all electrocardiographic (ECG) Signal processing. The baseline wander is caused
by varying electrode- skin impedance, patient’s movements and breath. The kind of
disturbances is especially present in exercise electrocardiography, as well as during
ambulatory and holter monitoring. The ECG signal is also degraded by additive 50 or 60 Hz
power line (AC) interference. These two artifacts are the dominant artifacts and strongly
affect the ST segment, degrade the signal quality, frequency resolution, produces large
amplitude signals in ECG that can resemble PQRST waveforms and masks tiny features that
are important for clinical monitoring and diagnosis. ECG in the primary input. Finally, we
have applied this algorithm on ECG Signals from the MIT-BIH data base and compared its
performance with the conventional LMS algorithm The goal of ECG Signal enhancement is
to separate the valid signal components from the undesired artifacts, so as to present an ECG
that facilitates easy and accurate interpretation. Many approaches have been reported in the
literature of address ECG enhancement using both adaptive and non-adaptive [1]-[6],
adaptive filtering techniques permit to the detect time varying potentials and to track the
dynamic variations of the signals. In [2], proposed and LMS based adaptive recurrent filter
to acquire the impulse response of normal QRS complexes and then applied it for arrhythmia
detection in ambulatory ECG recordings part from these several adaptive signal processing
techniques are also published, e.g. NLMS algorithm with decreasing step size, which
converge to the global minimum [3], a variable step size NLMS algorithm with faster
convergence rate [4],Costa et al. in [4] proposed a noise resilient variable step size, LMS
which is specially indicated for biomedical application, also several modification are
presented in literature to improve the performance of the LMS algorithm [5]- [8], recently in
[9] presented several less computational complex adaptive algorithms in time domain but
these algorithms exhibits slower convergence rate. The considered CSLMS algorithm is
based o the concept of difference quantities and the constraint of equilibrium in the sequence
of a posteriori estimation errors [10]. The method which applies nonlinearities to the error
and input signal sequences, which can be derived using the Lagrange multiplier method as a
generalization of the normalized LMS (NLMS) under certain conditions the adaptive noise
cancellers (ANC) based on the CSLMS algorithm shows improved performance by
decreasing the excess mean squared error and maladjustment compared to conventional
algorithms like, LMS and NLMS algorithms.
ICONCLUSION
In this paper the process of noise removal from ECG signal using CSLMS based The proposed treatment exploits the modifications in the weight update formula and thus
pushes up the speed over the respective LMS based realizations. Out simulation, however,
confirm that the performance of the CSLMS is better than the LMS algorithm in terms of
SNRI, MSE and maladjustment, this is shown in tables I and II. Hence CSLMS base adaptive
noise canceller may be used in all practical applications.
adaptive filtering is presented.