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Book ChapterDOI

Adaptive Kalman filter approach and Butterworth filter technique for ECG signal enhancement

01 Jan 2018-pp 315-322

TL;DR: For better diagnosis the authors need exact and consistent tools for determine the fitness of human hearts to analysis the disease ahead of time before it makes around an undesirable changes in human body.

AbstractAbout 15 million people alive today have been influenced by coronary illness. This is a major and critical issue in recent days. There are so many people have been lost their lives due to heart attack and other heart related issues. So, early on analysis and proper cure of heart disease is required to minimize the death rate due to heart disease. For better diagnosis we need exact and consistent tools for determine the fitness of human hearts to analysis the disease ahead of time before it makes around an undesirable changes in human body. For heart diagnosis one of the tools is Electrocardiogram (ECG) and the obtained signal is labeled ECG signal. This ECG signal contaminated by an amount of motion artifacts and noisy elements and deduction of these noisy elements from ECG signal must important before the ECG signal could be utilized for illness diagnosis purpose. There are various filter methods available for denoising ECG signal and select the best one on the dependence of performance parameter like signal to noise ratio (SNR) and power spectrum density (PSD).

Topics: Kernel adaptive filter (53%), Adaptive filter (53%), Digital filter (51%), Filter design (51%), Signal-to-noise ratio (51%)

Summary (1 min read)

1 Introduction

  • Observation of the ECG has quite some time been utilized as a part of clinical practice.
  • With advancement in sensor innovation such as material and capacitive terminals, sensors are fused in pieces of the incubator have ended up accessible [2].
  • Sometimes these ECG monitoring technologies contaminated due to breathing, and mismatching measurement and explanation of the signal’s components therefore, noise artifacts generated into acquired ECG signal and corrupt it.
  • Elimination of Interference in the ECG signal by using various filtering method such as, Adaptive filter and Weiner filter are utilized for removal artifacts from ECG signal [4].

3 Simulations and Result

  • The proposed approaches implemented in Matlab version 2009.
  • To study the performance of the planned method numerous standard data sets have been taken from physio.net, including the MIT-BIH database.
  • Power spectrum density (PSD) of Kalman filter response (b) Graphical representation of Butterworth filter method Fig.5 Filtered ECG signal using Butterworth filter Fig.7.
  • The power spectrum density (PSD) and signal to noise ratio (SNR) coming out from distinction between the original signal and signal obtained from each filter methods are given away in Table 1.
  • As can be seen from the table over, the level of contortion is negligible for the Kalman filter technique.

4 Discussion

  • The paper introduced a technique utilized as a part of present time noise artifacts removal.
  • As exposed in the outcomes, the KF approach had insignificant contortion, especially in the QRS complex fragment, when contrasted with the IIR Butterworth filter.
  • The KF approach for the elimination of noise artifacts is best for signal enhancement.
  • High SNR and PSD refers for high stability of the signal So, KF approach has ability to reduce distortion of ECG signal.
  • Butterworth has less SNR and PSD because it is highly dependent on filter order.

5 Conclusion

  • This paper proposed Electrocardiogram denoising execution utilizing Kalman filter and IIR Butterworth filter approach.
  • Simulation Results recover the denoising execution of both Kalman Filter and Butterworth filters are completely different because the performance parameters such as SNR and PSD of Kalman filter has much improved than Butterworth filter.
  • Priya Krishnamurthy, N. Swethaanjali, M. Arthi Bala Laxshmi: Comparison of Various Filtering Techniques Used For Removing High Frequency Noise in ECG Signal.

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Adaptive Kalman Filter Approach and Butterworth
Filter Technique for ECG Signal Enhancement
Bharati Sharma
1,1
, R. Jenkin Suji
1
and Amlan Basu
1
1
Department of Electronics and Communication Engineering, ITM University
Gwalior, Madhya Pradesh - 474001, INDIA
bharatisharma30@gmail.com, sujijenkin@gmail.com,
er.basu.amlan@gmail.com
Abstract. About 15 million people alive today have been influenced by
coronary illness. This is a major and critical issue in recent days. There are so
many people have been lost their lives due to heart attack and other heart related
issues. So, early on analysis and proper cure of heart disease is required to
minimize the death rate due to heart disease. For better diagnosis we need exact
and consistent tools for determine the fitness of human hearts to analysis the
disease ahead of time before it makes around an undesirable changes in human
body. For heart diagnosis one of the tools is Electrocardiogram (ECG) and the
obtained signal is labeled ECG signal. This ECG signal contaminated by an
amount of motion artifacts and noisy elements and deduction of these noisy
elements from ECG signal must important before the ECG signal could be
utilized for illness diagnosis purpose. There are various filter methods available
for denoising ECG signal and select the best one on the dependence of
performance parameter like signal to noise ratio (SNR) and power spectrum
density (PSD).
Keywords: Electrocardiogram (ECG), Kalman filter, Butterworth filter,
denoising, signal to noise ratio (SNR), power spectrum density (PSD).
1 Introduction
Observation of the ECG (Electrocardiogram) has quite some time been utilized as a
part of clinical practice. As of late, the relevance area of ECG observation is
extending to regions outer the laboratory [1]. Home observing of patients with rest
apnea is one of an example of such an area. There are various ECG monitoring
technology available, a move in ECG observing applications is occurring. With
advancement in sensor innovation such as material and capacitive terminals, sensors
are fused in pieces of the incubator have ended up accessible [2].
Another sensor technology brings the solace of the patient is enhancing continuously.
While a few years back the patient needed to accommodate itself according to
discomforts of the only available technology, but now a day’s patient used to new
technology for ECG monitoring and goes with a comfortable treatment of heart
diseases [3].
Sometimes these ECG monitoring technologies contaminated due to breathing, and
mismatching measurement and explanation of the signal’s components therefore,
noise artifacts generated into acquired ECG signal and corrupt it. Due to certain stress
test, the noise artifacts vary unpredictably. Elimination of Interference in the ECG
signal by using various filtering method such as, Adaptive filter and Weiner filter are
utilized for removal artifacts from ECG signal [4]. An adaptive Wavelet Weiner

filtering of ECG signals has been proposed with stationary Wavelet Transform (SWT)
and Wavelet Filtering method (WF) compared by different thresholding strategies[5].
This work presents an Adaptive Kalman filter and butter worth filter approach for the
estimation and denoising ECG signal. These IIR methods will be utilized for
approving the proposed Kalman filter approach.
The proposed procedure in this paper is a utilization of the Kalman filter (KF) for the
estimation and evacuation of the noise artifacts in ECG signal. The projected IIR filter
methods based on the frequency selective components [6]. The state space model is
coordinated with Kalman filter so as to approximate the state variables. The proposed
technique recommends an appropriate way for estimation of the noise artifacts of an
ECG signal, and is contrasted with the IIR filter methods which basis on the
performance parameters.
2 Methodologies
2.1 Kalman filter
An adaptive Kalman Filter (KF) is a recursive prescient filter that depends on the state
model and time varying recursive algorithms. An ECG signals complexes that relates
from back to back heartbeats are fundamentally the same yet not indistinguishable.
However, while the recording of ECG, the signal is defiled due to some noise
interference. An adaptive Kalman filter appraises the state of a dynamic system. This
dynamic system can be contaminated by noise. The Kalman filter utilizes estimations
to enhance the estimated state [7].
The KF method consists of the prediction and correction of states of the system.
x
t+1
= x
t
+ v
t
(1)
Yt+1 =
x
t+1
+ w
t+1
(2)
Where x
t+1
is the state input of the system, v
t
is the process noise of the system,
Y
t+1
is the measurement output of the system and w
t+1
is the measurement noise such as
noise artifacts.
The prediction is an initial work of the Kalman filter. The predict state or prior state
is intended by neglecting the noise of the system. In linear case state vector equation
can be represented as:-
X(t) = F. x(t) + n(t) (3)
X(t) = F. x(t) (4)
Where, F is the dynamic grid and is consistent, state vector x(t) and dynamic
interference(t) of the system.
The genuine predicted state is a linear combination of the primary state x
(t
0
)
From equation (3) & (4)
x
(t ) = A
t
0
x
(t
0
) (5)
Where, A
t
0
is called the conversion matrix, which transform the primary state x
(t
0
)
to its equivalent x
(t ) at point t.

Covariance matrix P
-
( t
i
) of the predicted state vector is attained with the law of error
transmission,
P
-
( t
i
) = A . P(t
i-1
) . A
T
+ Q (6)
Where, covariance matrix of the noise Q is a utility of time.
In a correction process we obtained the improved predicted state with observation
form at time t
i
, thus the posteriori state has form,
x
+
(t
i
) = x
-
(t
i
) + x(t
i
) (7)
And covariance matrix, P
+
(t
i
) = P
-
(t
i
) + P(t
i
)
(8)
x(t
i
) = K(t
i
) . [ l(t
i
)-l
-
(t
i
) ] (9)
K(t) = P
-
H
T
(HP
-
H
T
+ R(t
i
))
-1
(10)
Where, K is called the gain matrix. The difference [ l(t
i
)-l
-
(t
i
) ]is identified the extent
residual. It reproduces the inconsistency between the predicted measurement and
actual extent. At the end corrected state is received by,
x
+
(t
i
) = x
-
(t
i
) + x(t
i
). (11)
The Kalman Filter approach uses to predict and remove the noise artifacts from ECG
signal. Though, the equation given in (11) is repeated over input signal. Equation (11)
is updated for the Kalman filter.
2.2 Butterworth filter
Butterworth filters are having an attribute of maximally level recurrence response and
no ripples in the pass band. It moves of nears zero in the stop band [8]. Its reaction
inclines off directly towards negative infinity on bode plot. For example, other filter
types which have non-monotonic swell in the pass band or stop band, these filters are
having a monotonically changing size capacity with ω. The initial 2n-1 subordinates
for the force capacity as for recurrence are zero. Thus it is conceivable to determine
the formula for frequency response,
󰇛

󰇜
󰇛󰇜

(12)
3 Simulations and Result
The proposed approaches implemented in Matlab version 2009. To study the
performance of the planned method numerous standard data sets have been taken
from physio.net, including the MIT-BIH database. This ECG signal corrupted with
some noise artifacts and corrupted ECG signal passes through filter and get noise free
signal.

Fig.1 Typical ECG signal
Fig.2 Noisy ECG signal

a) Graphical representation of Kalman filter method
Fig.3 Kalman filter response of ECG signal
Figure 3 illustrates the Kalman filter response of ECG signal and blue line shows the
true response of the filter and red shows the filtered response of the signal.
Fig.4 Power spectrum density (PSD) of Kalman filter response

Citations
More filters

Book ChapterDOI
29 Apr 2021
Abstract: The emergence of Artificial Intelligence (AI) has brought many advancements in biomedical signal processing and analysis. It has opened the way for having efficient systems in the diagnosis and treatment of diseases such as Cardiovascular (CV) disorder. CV disorder is one of the critical health problems causing death to lots of peoples globally. Electrocardiogram (ECG) signal is the signal taken from the human body to diagnosis the status of CV and heart conditions. Earlier to the introduction of computers, the diagnosis of heart conditions was made by experts manually and that caused various mistakes. Currently, the usage of advancing signal processing devices help to reduce those errors and enables to develop effective signal detection and parameter estimation algorithms that are useful to analyze the parameters of ECG signals. Which intern supports to decide if the person is in critical condition and take an appropriate action. In this work, we analyze the performances of classical techniques and machine learning algorithms for ECG based CV parameters estimation. For this, first an in-depth review is done for both classical techniques and machine learning algorithms. Specifically, the benefits and challenges of machine learning and deep-learning algorithms for CV signal processing and parameter estimation is discussed. Then, we evaluate the performances of both classical (Kalman Filtering) and machine learning algorithms. The machine learning based algorithms are modeled with Butterworth low pass filter, wavelet transform and linear regression for parameter estimation. Besides, we propose an algorithm that combines adaptive Kalman filter (AKF) and discrete wavelet transform (DWT). In this algorithm, the ECG signal is filtered using AKF. Then, segmentation is performed and features are extracted by using DWT. Numerical simulation is done to validate the performances of these algorithms. The results show that at \({20}{\%}\) false positive rate, the detection performance of Kalman filtering, the proposed algorithm and machine learning algorithm are \({83}{\%}\), \({94}{\%}\) and \({97}{\%}\), respectively. That shows the proposed algorithm gives better performance than classical Kalman filtering and has nearly the same performance with machine learning algorithms.

References
More filters

Journal ArticleDOI
TL;DR: The authors provide an overview of these recent developments as well as of formerly proposed algorithms for QRS detection, which reflects the electrical activity within the heart during the ventricular contraction.
Abstract: The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.

1,262 citations


"Adaptive Kalman filter approach and..." refers methods in this paper

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Journal ArticleDOI
TL;DR: Efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure are presented, suitable for a hybrid system that integrates these algorithmic approaches for clean ECG data storage or transmission scenarios with high output SNRs, high CRs, and low distortions.
Abstract: This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For denosing, the SNR improvement criterion is used, while for compression, we have considered the compression ratio (CR), the percentage area difference (PAD), and the weighted diagnostic distortion (WDD) measure. Several Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG databases are used for performance evaluation. Simulation results illustrate that both applications can contribute to and enhance the clinical ECG data denoising and compression performance. For denoising, an average SNR improvement of 10.16 dB was achieved, which is 1.8 dB more than the next benchmark methods such as MAB WT or EKF2. For compression, the algorithm was extended to include more than five Gaussian kernels. Results show a typical average CR of 11.37:1 with WDD < 1.73 %. Consequently, the proposed framework is suitable for a hybrid system that integrates these algorithmic approaches for clean ECG data storage or transmission scenarios with high output SNRs, high CRs, and low distortions.

202 citations


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Journal ArticleDOI
TL;DR: A sequential averaging filter is developed that adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal, which demonstrates that, without using a priori knowledge on signal characteristics, the Filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance.
Abstract: The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a Bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the Bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.

132 citations


Journal ArticleDOI
TL;DR: This study focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation and used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise- free signal.
Abstract: In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.

93 citations


Journal ArticleDOI
Abstract: A wide area of research has been done in the field of noise removal in Electrocardiogram signals.. Electrocardiograms (ECG) play an important role in diagnosis process and providing information regarding heart diseases. In this paper, we propose a new method for removing the baseline wander interferences, based on discrete wavelet transform and Butterworth/Chebyshev filtering. The ECG data is taken from non-invasive fetal electrocardiogram database, while noise signal is generated and added to the original signal using instructions in MATLAB environment. Our proposed method is a hybrid technique, which combines Daubechies wavelet decomposition and different thresholding techniques with Butterworth or Chebyshev filter. DWT has good ability to decompose the signal and wavelet thresholding is good in removing noise from decomposed signal. Filtering is done for improved denoising performence. Here quantitative study of result evaluation has been done between Butterworth and Chebyshev filters based on minimum mean squared error (MSE), higher values of signal to interference ratio and peak signal to noise ratio in MATLAB environment using wavelet and signal processing toolbox. The results proved that the denoised signal using Butterworth filter has a better balance between smoothness and accuracy than the Chebvshev filter.

15 citations


"Adaptive Kalman filter approach and..." refers methods in this paper

  • ...Elimination of Interference in the ECG signal by using various filtering method such as, Adaptive filter and Weiner filter are utilized for removal artifacts from ECG signal [4]....

    [...]


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Chavan et al. this paper proposed an adaptive Kalman filter and butter worth filter approach for the estimation and denoising ECG signal.