The electrocardiogram (ECG) signals which are extensively used for heart disease diagnosis and patient monitoring are usually corrupted with various sources of noise. In this paper, an algorithm is developed to de-noise ECG signals based on Empirical Mode Decomposition (EMD) with application of Higher Order Statistics (HOS). The algorithm is applied on several ECG signals for different levels of Signal to Noise Ratio (SNR). The SNR improvement (SNRimp) and Percent Root mean square Difference (PRD (%)) are analyzed. The results show that the developed algorithm is a reasonable one to de-noise ECG signals.
In many situations, the Electrocardiogram (ECG) is
recorded during ambulatory or strenuous conditions such that the
signal is corrupted by different types of noise, sometimes
originating from another physiological process of the body. Hence,
noise removal is an important aspect of signal processing. Here five
different filters i.e. median, Low Pass Butter worth, FIR, Weighted
Moving Average and Stationary Wavelet Transform (SWT) with
their filtering effect on noisy ECG are presented. Comparative
analyses among these filtering techniques are described and
statically results are evaluated.
Performance comparison of automatic peak detection for signal analyserjournalBEEI
The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD. For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Estimation of Separation and Location of Wave Emitting Sources : A Comparison...sipij
A mathematical model for localization of acoustical sources with separation between them is derived and
presented. A classical ( Fourier transform ) method and a modern ,parametric , ( Burg ) method are used .
The results show the capability of Burg method to resolve the adjacent sources when compared with
Fourier transform method, as well as the localization of the sources . The performance is studies with
varying some parameters relating to the problem .
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Filtering Electrocardiographic Signals using filtered- X LMS algorithmIDES Editor
In this paper, a simple and efficient filtered- X
Least Mean Square (FXLMS) algorithm is used for the
removal of different kinds of noises from the ECG signal. 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 ECG in the primary input. Different
filter structures are presented to eliminate the diverse forms
of noise: baseline wander, 60 Hz power line interference,
muscle artifacts and motion artifacts. Finally different
adaptive structures are implemented to remove artifacts from
ECG signals and tested on real signals obtained from MITBIH
data base. Simulation studies shows that the proposed
realization gives better performance compared to existing
realizations in terms of signal to noise ratio.
In many situations, the Electrocardiogram (ECG) is
recorded during ambulatory or strenuous conditions such that the
signal is corrupted by different types of noise, sometimes
originating from another physiological process of the body. Hence,
noise removal is an important aspect of signal processing. Here five
different filters i.e. median, Low Pass Butter worth, FIR, Weighted
Moving Average and Stationary Wavelet Transform (SWT) with
their filtering effect on noisy ECG are presented. Comparative
analyses among these filtering techniques are described and
statically results are evaluated.
Performance comparison of automatic peak detection for signal analyserjournalBEEI
The aim of this paper is to propose a new peak detection method for a portable device, which know as modified automatic threshold peak detection (M-ATPD). M-ATPD evolves out of ATPD with a focus on reducing computational time. The proposed method replaces the clustering threshold calculation in ATPD with a standard deviation threshold calculation. M-ATPD reduces computational time by 2 times faster compared to ATPD for control signal and 8.65 times faster compared to ATPD for raw biosignals. Modified ATPD also shows a slight improvement in terms of detection error, with a decrease of about 6.66% to 13.33% in peak detection of noise signals. Modified ATPD successfully fixes the error of peak detection on pulse control signals associated with ATPD. For raw biosignals, in total M-ATPD achieved 19.41% lower detection error compare to ATPD.
Estimation of Separation and Location of Wave Emitting Sources : A Comparison...sipij
A mathematical model for localization of acoustical sources with separation between them is derived and
presented. A classical ( Fourier transform ) method and a modern ,parametric , ( Burg ) method are used .
The results show the capability of Burg method to resolve the adjacent sources when compared with
Fourier transform method, as well as the localization of the sources . The performance is studies with
varying some parameters relating to the problem .
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Filtering Electrocardiographic Signals using filtered- X LMS algorithmIDES Editor
In this paper, a simple and efficient filtered- X
Least Mean Square (FXLMS) algorithm is used for the
removal of different kinds of noises from the ECG signal. 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 ECG in the primary input. Different
filter structures are presented to eliminate the diverse forms
of noise: baseline wander, 60 Hz power line interference,
muscle artifacts and motion artifacts. Finally different
adaptive structures are implemented to remove artifacts from
ECG signals and tested on real signals obtained from MITBIH
data base. Simulation studies shows that the proposed
realization gives better performance compared to existing
realizations in terms of signal to noise ratio.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction IOSR Journals
Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary
and non-linear processes. The method does not require any pre & post processing of signal and use of any
specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a
new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. The presented
paper gives the detail of EEMD and its application in various fields. EEMD is a time–space analysis method, in
which the added white noise is averaged out with sufficient number of trials; and the averaging process results
in only the component of the signal (original data). EEMD is a truly noise-assisted data analysis (NADA)
method and represents a substantial improvement over the original EMD.
This paper aims to present a very-large-scale integration (VLSI) friendly electrocardiogram (ECG) QRS detector for body sensor networks. Baseline wandering and background noise are removed from original ECG signal by mathematical morphological method. The performance of the algorithm is evaluated with standard MIT-BIH arrhythmia database and wearable exercise ECG Data. Corresponding power and area efficient VLSI architecture is reduced by replacing the one of the Ripple Carry Adder in the Carry select adder with Binary to Excess 1 converter
Method for Converter Synchronization with RF InjectionCSCJournals
This paper presents an injection method for synchronizing analog to digital converters (ADC). This approach can eliminate the need for precision routed discrete synchronization signals of current technologies, such as JESD204. By eliminating the setup and hold time requirements at the conversion (or near conversion) clock rate, higher sample rate systems can be synchronized. Measured data from an existing multiple ADC conversion system was used to evaluate the method. Coherent beams were simulated to measure the effectiveness of the method. The results show near theoretical coherent processing gain.
Chaotic signals denoising using empirical mode decomposition inspired by mult...IJECEIAES
Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.
Research on Space Target Recognition Algorithm Based on Empirical Mode Decomp...Nooria Sukmaningtyas
The space target recognition algorithm, which is based on the time series of radar cross section
(RCS), is proposed in this paper to solve the problems of space target recognition in the active radar
system. In the algorithm, EMD method is applied for the first time to extract the eigen of RCS time series.
The normalized instantaneous frequencies of high-frequency intrinsic mode functions obtained by EMD are
used as the eigen values for the recognition, and an effective target recognition criterion is established.
The effectiveness and the stability of the algorithm are verified by both simulation data and real data. In
addition, the algorithm could reduce the estimation bias of RCS caused by inaccurate evaluation, and it is
of great significance in promoting the target recognition ability of narrow-band radar in practice.
Optimum range of angle tracking radars: a theoretical computingIJECEIAES
In this paper, we determine an optimal range for angle tracking radars (ATRs) based on evaluating the standard deviation of all kinds of errors in a tracking system. In the past, this optimal range has often been computed by the simulation of the total error components; however, we are going to introduce a closed form for this computation which allows us to obtain the optimal range directly. Thus, for this purpose, we firstly solve an optimization problem to achieve the closed form of the optimal range (Ropt.) and then, we compute it by doing a simple simulation. The results show that both theoretical and simulation-based computations are similar to each other.
Papr reduction for ofdm oqam signals via alternative signal methodeSAT Journals
Abstract
We deemed the PAPR reduction problem for OFDM/OQAM system. The PAPR reduction is the serious problem for
implementations of both OFDM and OFDM/OQAM systems due to their high PAPR. The OFDM/OQAM signal is generated by
summing over M time-shifted OFDM/OQAM symbols, where the successive symbols are interdependent with each other. The AS
(Alternative-Signal) method directly leads to the independent AS (AS-I) and joint AS (AS-J) algorithms. The AS-I algorithm
reduces the PAPR symbol by symbol with low complexity and AS-J applies optimal joint PAPR reduction among M
OFDM/OQAM symbols with much higher complexity. A sequential optimization procedure denoted AS-S have been proposed to
balance the computation complexity and system performance in this paper. AS-S algorithm, which adopts a sequential
optimization procedure over time with computational complexity linearly increasing with M. The Simulation results have been
provided for performance comparison of AS-I, AS-J, and AS-S algorithms.
Keywords—Peak-to-Average power ratio (PAPR), orthogonal frequency division multiplexing with offset quadrature
amplitude modulation (OFDM/OQAM), Alternative-signal(AS),cyclic prefix(CP).
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...jantjournal
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation
of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a
49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction IOSR Journals
Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary
and non-linear processes. The method does not require any pre & post processing of signal and use of any
specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a
new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. The presented
paper gives the detail of EEMD and its application in various fields. EEMD is a time–space analysis method, in
which the added white noise is averaged out with sufficient number of trials; and the averaging process results
in only the component of the signal (original data). EEMD is a truly noise-assisted data analysis (NADA)
method and represents a substantial improvement over the original EMD.
This paper aims to present a very-large-scale integration (VLSI) friendly electrocardiogram (ECG) QRS detector for body sensor networks. Baseline wandering and background noise are removed from original ECG signal by mathematical morphological method. The performance of the algorithm is evaluated with standard MIT-BIH arrhythmia database and wearable exercise ECG Data. Corresponding power and area efficient VLSI architecture is reduced by replacing the one of the Ripple Carry Adder in the Carry select adder with Binary to Excess 1 converter
Method for Converter Synchronization with RF InjectionCSCJournals
This paper presents an injection method for synchronizing analog to digital converters (ADC). This approach can eliminate the need for precision routed discrete synchronization signals of current technologies, such as JESD204. By eliminating the setup and hold time requirements at the conversion (or near conversion) clock rate, higher sample rate systems can be synchronized. Measured data from an existing multiple ADC conversion system was used to evaluate the method. Coherent beams were simulated to measure the effectiveness of the method. The results show near theoretical coherent processing gain.
Chaotic signals denoising using empirical mode decomposition inspired by mult...IJECEIAES
Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.
Research on Space Target Recognition Algorithm Based on Empirical Mode Decomp...Nooria Sukmaningtyas
The space target recognition algorithm, which is based on the time series of radar cross section
(RCS), is proposed in this paper to solve the problems of space target recognition in the active radar
system. In the algorithm, EMD method is applied for the first time to extract the eigen of RCS time series.
The normalized instantaneous frequencies of high-frequency intrinsic mode functions obtained by EMD are
used as the eigen values for the recognition, and an effective target recognition criterion is established.
The effectiveness and the stability of the algorithm are verified by both simulation data and real data. In
addition, the algorithm could reduce the estimation bias of RCS caused by inaccurate evaluation, and it is
of great significance in promoting the target recognition ability of narrow-band radar in practice.
Optimum range of angle tracking radars: a theoretical computingIJECEIAES
In this paper, we determine an optimal range for angle tracking radars (ATRs) based on evaluating the standard deviation of all kinds of errors in a tracking system. In the past, this optimal range has often been computed by the simulation of the total error components; however, we are going to introduce a closed form for this computation which allows us to obtain the optimal range directly. Thus, for this purpose, we firstly solve an optimization problem to achieve the closed form of the optimal range (Ropt.) and then, we compute it by doing a simple simulation. The results show that both theoretical and simulation-based computations are similar to each other.
Papr reduction for ofdm oqam signals via alternative signal methodeSAT Journals
Abstract
We deemed the PAPR reduction problem for OFDM/OQAM system. The PAPR reduction is the serious problem for
implementations of both OFDM and OFDM/OQAM systems due to their high PAPR. The OFDM/OQAM signal is generated by
summing over M time-shifted OFDM/OQAM symbols, where the successive symbols are interdependent with each other. The AS
(Alternative-Signal) method directly leads to the independent AS (AS-I) and joint AS (AS-J) algorithms. The AS-I algorithm
reduces the PAPR symbol by symbol with low complexity and AS-J applies optimal joint PAPR reduction among M
OFDM/OQAM symbols with much higher complexity. A sequential optimization procedure denoted AS-S have been proposed to
balance the computation complexity and system performance in this paper. AS-S algorithm, which adopts a sequential
optimization procedure over time with computational complexity linearly increasing with M. The Simulation results have been
provided for performance comparison of AS-I, AS-J, and AS-S algorithms.
Keywords—Peak-to-Average power ratio (PAPR), orthogonal frequency division multiplexing with offset quadrature
amplitude modulation (OFDM/OQAM), Alternative-signal(AS),cyclic prefix(CP).
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...jantjournal
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation
of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a
49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
IOSR Journal of Mathematics(IOSR-JM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IOSR Journal of Business and Management (IOSR-JBM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The effect of rotational speed variation on the static pressure in the centri...IOSR Journals
The current investigation is aimed to simulate the three-dimensional complex internal flow in a
centrifugal pump impeller with five twisted blades by using specialized computational fluid dynamics (CFD)
software ANSYS /FLUENT 14code with a standard k-ε two-equation turbulence model.
A single blade passage will be modeled to give more accurate results for static pressure contours on (blade,
hub, and shroud). The potential consequences of static pressure associated with operating a centrifugal
compressor in variable rotation speed.
A numerical three-dimensional, through flow calculations to predict static pressure through a
centrifugal pump were presented to examined the effect of rotational speed variation on the static pressure of
the centrifugal pump . The contours of the static pressure of the blade, hub, and shroud indicates negative low
static pressure in the suction side at high rotational speed (over operation limits )and the static pressure
increases gradually until reach maximum value at the leading edge (6×105 Pa) of the blade.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A new approach for Reducing Noise in ECG signal employing Gradient Descent Me...paperpublications3
Abstract: ECG is the main tool used by the physicians for identifying and for interpretation of Heart condition. The ECG should be free from noise and of good quality for the correct diagnosis. In real time situations ECG are corrupted by many types of noises. The high frequency noise is one of them. In this thesis, analysis has been carried out the use of neural network for denoising the ECG signal. A multilayer artificial neural network (ANN) is designed. Here gradient descent method (GDM) is used for training of artificial neural network. The noisy ECG signal is given as input to the neural network. The output of neural network is compared with De-noised(original) ECG signal and value of Root Mean Square Error(RMSE) is computed. In training process the weights are updated until the value of RMSE is minimized. Several iteration has to be performed in order to find Minimum Mean Square Error(MMSE). At MMSE network weights are finalized. Subsequently, network parameters are used for Noise reduction. The comparison with other technique shows that the neural networks method is able to better preserve the signal waveform at system output with reduced noise. Our results shows better accuracy in terms of parameters root mean square error, signal to noise ratio and smoothness (RMSE,SNR and R) as compare to GOWT[18].The database has been collected from MIT-BIH arrhythmias database.
Electrocardiogram Denoised Signal by Discrete Wavelet Transform and Continuou...CSCJournals
One of commonest problems in electrocardiogram (ECG) signal processing is denoising. In this paper a denoising technique based on discrete wavelet transform (DWT) has been developed. To evaluate proposed technique, we compare it to continuous wavelet transform (CWT). Performance evaluation uses parameters like mean square error (MSE) and signal to noise ratio (SNR) computations show that the proposed technique out performs the CWT.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reductionIOSR Journals
Abstract:Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary and non-linear processes. The method does not require any pre & post processing of signal and use of any specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. The presented paper gives the detail of EEMD and its application in various fields. EEMD is a time–space analysis method, in which the added white noise is averaged out with sufficient number of trials; and the averaging process results in only the component of the signal (original data). EEMD is a truly noise-assisted data analysis (NADA) method and represents a substantial improvement over the original EMD. Keywords –Data analysis, Empirical mode decomposition, intrinsic mode function, mode mixing, NADA,
Beats classification is an essential step in the ECG signal analysis for cardiac arrhythmias detection. There are multiple alternatives to solve this problem, but these are considerably reduced when re-al-time restrictions are added to the analysis. The goal of this work is to expose an optimal solution based mainly on the use of voltage values of the signal in the time domain and compare it with other based on Daubechies’ Wavelets analysis. Several measures are used in both feature spaces to determine the similarity of every beat to a patient’s specific patterns and, after that, a method similar to clustering’s algorithms is used to assign a class to each.
ECG signal denoising using a novel approach of adaptive filters for real-time...IJECEIAES
Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.
Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
In this paper, the multi-channel electromyogram acquisition system is being developed using programmable
system on chip (PSOC) microcontroller to obtain the surface of EMG signal. The two pairs of single-channel
surface electrodes are utilized to measure the EMG signal obtained from forearm muscles. Then different levels
of Wavelet family are used to analyze the EMG signal. Later features in terms of root mean square, logarithm of
root mean square, centroid of frequency, as well as standard deviation were used to extract the EMG signal. The
proposed method of feature extraction for extracting EMG signal states that root means square feature extraction
method gives better performance as compared to the other features. In the near future, this method can be used to
control a mechanical arm as well as robotic arm in field of real-time processing.
ECG Signal Compression Technique Based on Discrete Wavelet Transform and QRS-...CSCJournals
In this paper, an Electrocardiogram (ECG) signal is compressed based on discrete wavelet transform (DWT) and QRS-complex estimation. The ECG signal is preprocessed by normalization and mean removal. Then, an error signal is formed as the difference between the preprocessed ECG signal and the estimated QRS-complex waveform. This error signal is wavelet transformed and the resulting wavelet coefficients are thresholded by setting to zero all coefficients that are smaller than certain threshold levels. The threshold levels of all subbands are calculated based on Energy Packing Efficiency (EPE) such that minimum percentage root mean square difference (PRD) and maximum compression ratio (CR) are obtained. The resulted thresholded DWT coefficients are coded using the coding technique given in [1], [20]. The compression algorithm was implemented and tested upon records selected from the MIT - BIH arrhythmia database [2]. Simulation results show that the proposed algorithm leads to high CR associated with low distortion level relative to previously reported compression algorithms [1], [14] and [18]. For example, the compression of record 100 using the proposed algorithm yields to CR = 25.15 associated with PRD = 0.7% and PSNR = 45 dB. This achieves compression rate of nearly 128 bit/sec. The main features of this compression algorithm are the high efficiency, high speed and simplicity in design.
New Method of R-Wave Detection by Continuous Wavelet TransformCSCJournals
In this paper we have employed a new method of R-peaks detection in electrocardiogram (ECG) signals. This method is based on the application of the discretised Continuous Wavelet Transform (CWT) used for the Bionic Wavelet Transform (BWT). The mother wavelet associated to this transform is the Morlet wavelet. For evaluating the proposed method, we have compared it to others methods that are based on Discrete Wavelet Transform (DWT). In this evaluation, the used ECG signals are taken from MIT-BIH database. The obtained results show that the proposed method outperforms some conventional techniques used in our evaluation.
Novel method to find the parameter for noise removal from multi channel ecg w...eSAT Journals
Abstract In general, electrocardiogram (ECG) waveforms are affected by noise and artifacts and it is essential to remove the noise in order to support any decision making for specialist. It is very difficult to remove the noise from 12 channel ECG waveforms using standard noise removal methodologies. Removal of the noise from ECG waveforms is majorly classified into two types in signal processing namely Digital filters and Analog filters. Digital filters are more accurate than analog filters because analog filters introduce nonlinear phase shift. Most advanced research digital filters are FIR and IIR.FIR filters are stable as they have non-recursive structure. They give the exact linear phase and efficiently realizable in hardware. The filter response is finite duration. Thus noise removal using FIR digital filter is better option in comparison with IIR digital filter. But it is very difficult to find the cut-off frequency parameter for dynamic multi-channel ECG waveforms using existing traditional methods. So, in this research, newly introduced Multi-Swarm Optimization (MSO) methodology for automatically identifying the cut-off frequency parameter of multichannel ECG waveforms for low-pass filtering is inspecting. Generally, the spectrums of the ECG waveforms are extracted from four classes: normal sinus rhythm, atria fibrillation, arrhythmia and supraventricular. Baseline wander is removed using the Moving Median Filter. A dataset of the extracted features of the ECG spectrums is used to train the MSO. The performance of the MSO with various parameters is investigated. Finally, the MSO-identified cut-off frequency parameter, it’s applied to a Finite Impulse Response (FIR) filter. The resulting signal is evaluated against the original clean and conventional filtered ECG signal. Keywords: 12 Channel ECG Waveforms, Multi Swarm Optimization Neural Network, Low-pass filtering, Finite Impulse Response (FIR).
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Rule Based Identification of Cardiac Arrhythmias from Enhanced ECG Signals Us...CSCJournals
The detection of abnormal cardiac rhythms, automatic discrimination from rhythmic heart activity, became a thrust area in clinical research. Arrhythmia detection is possible by analyzing the electrocardiogram (ECG) signal features. The presence of interference signals, like power line interference (PLI), Electromyogram (EMG) and baseline drift interferences, could cause serious problems during the recording of ECG signals. Many a time, they pose problem in modern control and signal processing applications by being narrow in-band interference near the frequencies carrying crucial information. This paper presents an approach for ECG signal enhancement by combining the attractive properties of principal component analysis (PCA) and wavelets, resulting in multi-scale PCA. In Multi-Scale Principal Component Analysis (MSPCA), the PCA’s ability to decorrelate the variables by extracting a linear relationship and wavelet analysis are utilized. MSPCA method effectively processed the noisy ECG signal and enhanced signal features are used for clear identification of arrhythmias. In MSPCA, the principal components of the wavelet coefficients of the ECG data at each scale are computed first and are then combined at relevant scales. Statistical measures computed in terms of root mean square deviation (RMSD), root mean square error (RMSE), root mean square variation (RMSV) and improvement in signal to noise ratio (SNRI) revealed that the Daubechies based MSPCA outperformed the basic wavelet based processing for ECG signal enhancement. With enhanced signal features obtained after MSPCA processing, the detectable measures, QRS duration and R-R interval are evaluated. By using the rule base technique, projecting the detectable measures on a two dimensional area, various arrhythmias are detected depending upon the beat falling into particular place of the two dimensional area.
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De-Noising Corrupted ECG Signals By Empirical Mode Decomposition (EMD) With Application of Higher Order Statistics (HOS)
1. IOSR Journal of Applied Physics (IOSR-JAP)
e-ISSN: 2278-4861.Volume 4, Issue 1 (May. - Jun. 2013), PP 47-52
www.iosrjournals.org
www.iosrjournals.org 47 | Page
De-Noising Corrupted ECG Signals By Empirical Mode
Decomposition (EMD) With Application of Higher Order
Statistics (HOS)
Mitra DJ1
, Shahjalal M2
, Kiber MA3
1.3
Department of Applied Physics, Electronics and Communication Engineering, University of Dhaka,
Bangladesh
2
Departments of Basic Science, Primeasia University, Banani, Dhaka, Bangladesh
Abstract: The electrocardiogram (ECG) signals which are extensively used for heart disease diagnosis and
patient monitoring are usually corrupted with various sources of noise. In this paper, an algorithm is developed
to de-noise ECG signals based on Empirical Mode Decomposition (EMD) with application of Higher Order
Statistics (HOS). The algorithm is applied on several ECG signals for different levels of Signal to Noise Ratio
(SNR). The SNR improvement (SNRimp) and Percent Root mean square Difference (PRD (%)) are analyzed. The
results show that the developed algorithm is a reasonable one to de-noise ECG signals.
Keywords: ECG, Empirical Mode Decomposition (EMD), Higher Order Statistics (HOS), Intrinsic Mode
Function (IMF)
I. Introduction
Biomedical signals reflect the nature and activities of physiological processes. The electrocardiogram
(ECG) is the electrical manifestation of the contractile activity of the heart. The ECG is essential for diagnosis,
and therefore management of abnormal cardiovascular activity. Noise or unwanted signal is always present in
ECG signal, which makes it difficult to analyze. The typical sources of noise are, high frequency noise, motion
artifacts in ECG, maternal interference in fetal ECG, EMG noise, instrumentation noise etc. Therefore de-
noising the ECG signal is a pre-requisite to arrive at proper diagnosis by analyzing it.
A number of methods have been applied to de-noise ECG signals such as, digital filters, ICA, PCA,
adaptive filtering, wavelet transform etc. The existing de-noising techniques have certain limitations. The filter
bank based de-noising process smoothes the P and R amplitude of the ECG signal, and it is more sensitive to
different levels of noise [1]; The statistical model derived in PCA, ICA is not only fairly arbitrary but also
extremely sensitive to small changes in either the signal or the noise unless the basis functions are trained on a
global set of ECG beat types, moreover, the ICA doesn’t allow the prior information about the signals for
efficient filtering; adaptive filtering requires reference signal information for the effective filtering process, and
the reference signal has to be additionally recorded together with ECG [2]. Wavelets need a basis function to be
specified, moreover, the hard-thresholding WT leads to oscillation of the reconstructed ECG signal, and the
soft-thresholding method reduce the amplitudes of the ECG waveform, especially reduce the amplitudes of the
R-waves which is more important to diagnose the heart diseases [3].
In this paper, an algorithm has been developed to de-noise ECG signal based on Empirical Mode
Decomposition (EMD) along with application of Higher Order Statistics (HOS). The EMD method is totally
adaptive and data driven. This method doesn’t need a-priori basis function selection (i.e. mother wavelet) for
signal decomposition. EMD is effectively used for signal de-noising in a wide range of applications, such as
acoustic signals, ionospheric signals, in the study of heart rate variability (HRV), analysis of respiratory
mechanomyographic signals, crackle sound analysis in lung sounds and enhancement of cardiograph signals.
The acceptance of the method as a processing tool is stressed by the large number of publications in diverse
areas of signal processing including financial applications, fluid dynamics, ocean engineering and
electromagnetic field time series analysis [4]. Thus EMD is a versatile method to de-noise and analyze non-
stationary signals.
II. Empirical Mode Decomposition (Emd)
Empirical Mode Decomposition (EMD) method is a new non-linear technique, which was first
formulated by Dr. Norden Huang of NASA in 1996, for adaptively representing non-stationary signals as sums
of zero mean AM-FM components [5]. This method has very good results in the analysis of non-linear and non-
stationary signals, especially to the exact representation of the energy of the signal and the frequency content
thereof in relation to time. The main feature of the new method is to analyze the signal in structural components
known as Intrinsic Mode Functions (IMFs), arising from the signal itself and not defined in advance, so that the
2. De-Noising Corrupted ECG Signals By EMD With Application Of HOS
www.iosrjournals.org 48 | Page
analysis can be considered not as a-priori, but instead a-posteriori. The final result is the ability to display the
spectrum of the signal in function of time, much more accurately than the traditional methods. An Intrinsic
Mode Function (IMF) represents the oscillating mode embedded in the original data. IMF is a function that
satisfies the following two conditions:
1. The total number of local extrema and the number of zero crossings should be equal to each other or differ by
at most 1.
2. At any point the mean of upper and lower envelopes respectively defined by local maxima and the local
minima should be zero.
2.1 Sifting Process: Extraction of IMF by EMD
The algorithm is as following [5]:
Step 1: For a signal x(t), create upper envelope Emax(t) by local maxima and lower envelope Emin(t) by local
minima by using cubic splines interpolation.
Step 2: Calculate the mean of the upper and lower envelope:
𝑚1 =
𝐸 𝑚𝑎𝑥 (𝑡)+𝐸 𝑚𝑖𝑛 (𝑡)
2
(1)
Step 3: Subtract the mean from original data:
ℎ1 𝑡 = 𝑥 𝑡 − 𝑚1(𝑡) (2)
Step 4: Verify that h1(t) satisfies conditions for IMFs. If not, repeat steps 1-4 and the new balance emerges as:
ℎ11 𝑡 = ℎ1 𝑡 − 𝑚11(𝑡) (3)
Where, 𝑚11 𝑡 is the mean value of the envelopes defined by the extremes of ℎ1 𝑡 .
Step 5: Get first IMF (after k iterations):
ℎ1𝑘 𝑡 = ℎ1 𝑘−1 𝑡 − 𝑚1𝑘 𝑡 (4)
Where, k is the number of repetitions until the first IMF,ℎ1𝑘 𝑡 = 𝐼𝑀𝐹1 , occurs.
Step 6: Calculate first residue:
𝑟1 𝑡 = 𝑥 𝑡 − ℎ1𝑘 (𝑡) (5)
Step 7: Repeat whole algorithm with r1(t), r2(t)…. until residue is monotonic function.
Step 8: After n iterations x(t) is decomposed according to equation,
𝑥 𝑡 = 𝐼𝑀𝐹𝑖 +𝑛
𝑖=1 𝑟𝑛 (6)
Higher Order Statistics (HOS)
The use of higher-order statistics provides insight into signals which is not always available at lower
orders. Additionally, Gaussian-distributed signals have the interesting characteristic of disappearing at higher
orders. Because so much of the noise and interference environment is Gaussian-distributed, higher order
statistics thus offer the promise of a useful method of noise reduction [6]. ECG signal is easily contaminated by
different sources of noises, as mentioned previously. Different noise sources can be approximated by a white
Gaussian noise source [7]. So, after EMD is used to decompose the ECG signal into its IMF, higher order
statistics is a good choice to remove any Gaussian scales from the signal. In this paper, Kurtosis and Bispectrum
of every IMF are used as HOS parameters to check the Gaussianity, which is then followed by Bootstrap
technique.
In probability theory and statistics Kurtosis is any measure of the peakedness of the probability
distribution of a real-valued random variable. Kurtosis is defined as the normalized version of the fourth order
Cumulant. Assuming a zero mean signal, the normalized Kurtosis is expressed as:
𝐾4 =
𝐸 𝑥(𝑡)4
𝐸 𝑥(𝑡)2 2 − 3 = 𝑁
𝑥(𝑛)4𝑁
𝑛=1
𝑥(𝑛)2𝑁
𝑛=1
2 − 3 (7)
Where N is the number of signal samples, and n= 1, 2,……N
And, a Kurtosis estimator can take values as:
𝐾4 ≤
24
𝑁
1−𝑎
(8)
Where a is as authorized confidence percentage value, with a numerically estimated optimum equal to 90%.
However, even though the Kurtosis of a Gaussian signal is restricted by equation (8), in fact Kurtosis estimation
may still be invalid, especially when the samples of the signal are not numerous enough to ensure convergence.
In these cases, the solution comes in the form of bootstrap technique.
Bootstrap is a statistical method to increase the accuracy of the estimator, and it is very effective in
cases when the available signal samples are limited. Bootstrap does exactly what a scientist do in practice if it’s
possible: it repeats the experiment many times. Bootstrap randomly reassigns the observations, re-computes the
estimates many times and treats these reassignments as repeated experiments [8].
In this study Bootstrap is used to evaluate the Kurtosis of each of the signal IMFs right after EMD. The
number of the IMF samples reassignments is limited to 1000 for computational load purposes. The Bootstrap
3. De-Noising Corrupted ECG Signals By EMD With Application Of HOS
www.iosrjournals.org 49 | Page
algorithm gives a maximum and minimum Kurtosis estimation of the Kurtosis value for each of the signal IMFs,
which in turn is compared with the theoretical Kurtosis limit as it is calculated by equation (8).
Signal Bispectrum is another candidate to test Gaussianity. The Bispectrum is defined as the third
order Spectrum of the signal and is calculated either as the Fourier transform of its third order Cumulant, or as
the triple product of its Fourier coefficients, [9]. That is:
𝐵2
𝑥
𝜔1, 𝜔2 = 𝐸 𝑋 𝜔1 𝑋 𝜔2 𝑋∗
(𝜔1 + 𝜔2) = 𝑚3 𝑋 𝜔1 𝑋 𝜔2 𝑋∗
(𝜔1 + 𝜔2) (9)
Where, 𝜔1 ≤ 𝜋, 𝜔1 ≤ 𝜋, 𝜔1 + 𝜔2 ≤ 𝜋,
X(ω) is the Fourier transform of the signal x(n), and, m3 is the third order moment.
The Bispectrum of a Gaussian process is zero. However, there exist statistical processes where the
Bispectrum is zero despite deviating from Gaussianity. In other words a non Gaussian signal may admit zero
Bispectrum, but if a signal is Gaussian its Bispectrum has to be equal to zero. To this end, using the Bispectrum
criterion to ensure Gaussianity after the signal is classified as Gaussian by the Kurtosis test, appears to be a good
choice. Although the computational cost increases by using two Gaussianity estimators, by doing this it can be
ensured that the IMF under examination can be safely classified as Gaussian and excluded from the signal
reconstruction process. After the Gaussianity is checked thresholding rule is applied on the non-Gaussian IMFs.
The threshold is a modified version of the universal threshold proposed by Dohono [1], expressed as:
𝑇 = 𝑐 𝑉𝑖 2 ln(𝑁) (10)
Where, Vi is the noise variance estimated by the noise model for the i-th IMF, (i≥2), N is the number of
signal samples, and c is a constant experimentally found to take values 1 to 0.7 depending of the type of signal.
The assumption also that the total noise energy is captured by the first IMF is not valid in the general case,
therefore the noise variance for the first IMF is estimated using a better estimator as:
𝑉1 =
𝑚𝑒𝑑𝑖𝑎𝑛 𝐼𝑀𝐹1
0.6745
2
(11)
An alternative approach for the noise variance estimator is where the absolute median deviation of the first
IMF is taken into account as:
𝑉1 =
𝑚𝑒𝑑𝑖𝑎𝑛 ( 𝐼𝑀𝐹1−𝑚𝑒𝑑𝑖𝑎𝑛 (𝐼𝑀𝐹1) )
0.6745
2
(12)
A series of simulations concluded that the second of these two versions of noise variance estimator, performs
better for all types of signals.
Then the variance of each of the IMFs can be parameterized as a function of the first IMF variance as:
𝑉𝑖 =
𝑉1
𝛽 𝐻
𝜌 𝐻
−2(1−𝐻)𝑘
, 𝑘 ≥ 2 (13)
Where, βH is experimentally estimated in for three values of the Hurst exponent as (Η=0.2, βH=0.487),
Η=0.5, βH=0.719, (Η=0.8, βH=1.025), and, 𝑘 ≥ 2 and 𝜌 𝐻 ≈ 2.
Having now determined the thresholds for each IMF, the de-noising method would necessitate zeroing
the portion of the IMF which is below the threshold. However, the IMF nature requires setting to zero the IMF
portion between two adjacent zero crossings, when the absolute maximum of the IMF in this interval is below
the predefined threshold. This fact is based on the assumption that if the extrema which lies inside two adjacent
zero crossings interval exceeds the threshold, the interval is signal dominant; otherwise it is noise dominant
[10].
The thresholding operation for the EMD case and for every two successive zero crossings interval, 𝑧𝑖
𝑗
=
𝑧𝑖
𝑗
, 𝑧𝑖
𝑗 +1
, the hard-thresholding can be expressed as:
𝑧𝑖
𝑗
=
𝑧𝑖
𝑗
, 𝑟𝑖
𝑗
> 𝑇𝑖
0, 𝑟𝑖
𝑗
≤ 𝑇𝑖
(14)
Where, 𝑧𝑖
𝑗
denotes the thresholding interval, i is the IMF order index, 𝑟𝑖
𝑗
is the jth
extrema of the ith
IMF, and j
= 1, 2,…., (Mi - 1), with Mi being the number of the zero-crossings of the ith
IMF.
For the soft-thresholding cases 𝑧𝑖
𝑗
is given as:
𝑧𝑖
𝑗
=
𝑧𝑖
𝑗 𝑟𝑖
𝑗
−𝑇 𝑖
𝑟𝑖
𝑗 , 𝑟𝑖
𝑗
> 𝑇𝑖
0, 𝑟𝑖
𝑗
≤ 𝑇𝑖
(15)
The thresholded IMF is formed by concatenating the thresholded intervals as:
𝐼𝑀𝐹𝑖=[𝑧𝑖
1
, 𝑧𝑖
2
, 𝑧𝑖
3
, 𝑧𝑖
𝑗
] (16)
4. De-Noising Corrupted ECG Signals By EMD With Application Of HOS
www.iosrjournals.org 50 | Page
III. Proposed Algorithm to De-Noise ECG Signal
The main objects to suppress noise from an ECG signal are,
(a) Improving the signal to noise ratio (SNR), in order to unambiguously distinguish the characteristics of the
signal.
(b) Non-alteration of the original waveform shape and especially that of the complex QRS, preventing
deformation waves P and T, as well as maintaining proper ST area, so as maintain visibility of signal T.
Regarding these facts, the steps to de-noise ECG signal are as following,
Step 1: Empirical Mode Decomposition (EMD) of the ECG signal
Step 2: Delineation and separation of QRS complex
Step 3: Preservation of the QRS complex by using proper window
Step 4: Suppression of noise from intermediate portions of QRS complexes
Step 5: Checking Gaussianity of IMFs by higher order statistics (HOS)
Step 6: Thresholding the non-Gaussian IMFs and reconstruction of the ECG signal
IV. Implementation of The Algorithm
The developed algorithm is applied on ECG signals, which were taken from the Department of
Biomedical Physics and Technology, University of Dhaka. To observe the versatility of the technique the
algorithm is implemented on ECG signals of all 12 leads, and for different levels of input SNR. Fig. 1 and 2
show an uncorrupted signal and corrupted noisy signal. The noisy signal is decomposed into IMFs, which is
shown in Fig. 3.
The basic principle of de-noising by EMD is to represent the de-noised signal with a partial sum of the
IMFs. Although various approaches have been proposed to identify whether a specific IMF contains useful
information or noise, their performances are not satisfactory when directly applied to the problem of ECG de-
noising, as discussed next.
Examining the IMFs in Fig 3, it’s easy to find that the IMF1 contains almost nothing but high
frequency noise, and that the rest IMFs can be considered to mainly contain useful information about the ECG
components, except the IMF2 which contains both high frequency noise and components of the QRS complex.
Here comes the dilemma. If the IMF1 is simply discarded as noise, the output will still consist of considerable
noise as illustrated in Fig. 4(a). If the IMF2 is removed together, the resultant ECG will have the R waves
heavily distorted as shown in Fig. 4(b). Therefore, neither result is satisfactory.
The rate of information change in the QRS complex is very high compared to that of the other parts
of an ECG signal. An analysis of the EMD on clean and noisy ECG indicates that the QRS information is
mainly embedded in the first three high frequency IMFs. As a consequence, in a noisy case, a desirable
approach to de-noise the corrupted ECG signal y[n] in the EMD domain would be to filter out the noisy parts of
the first three IMFs without discarding the IMFs completely thus preserving the QRS complex. Now, adding
first three IMFs: d[n] = IMF1 + IMF2+ IMF3 is obtained from the corresponding ECG signal. Fig. 5 presents
the original uncorrupted and noisy ECG signals and the respective plots of d[n] in each case. It is revealed from
this figure that the oscillatory pattern of the QRS complex, and that of the d[n] in the QRS complex region are
highly similar to each other. So, QRS complex portion is the least affected part of ECG by noise.
So, the algorithm to delineate QRS complex is,
Step 1: Identify the fiducial points, which is the peaks of the R-wave
Step 2: Sum the first three IMFs to obtain d[n]
Step 3: Find the two nearest local minima on both sides of the fiducial point
Step 4: Detect the two closest zero-crossing points on the left-hand side of the left minimum and on the right-
hand side of the right minimum. These two points are identified as boundaries of the QRS complex. Next, a
window function is designed to preserve the QRS complex. The window function is a time domain window
applied to the sum of the first three IMFs, d[n]. A general design guideline for the QRS preserving window
function is that it should be flat over the duration of the QRS complex and decay gradually to zero so that a
smooth transition introduces minimal distortion. In this work, Tukey window is used. Fig. 6 shows the signal
d[n] after windowing operation.
Then the noise in the intermediate portions is suppressed by Savitzky-Golay(S-G) filter. S-G filter is
used because of its good reported processing of White Gaussian noise. Now, the Gaussianity of the IMFs is
checked according to HOS parameters discussed in section 4. The result of application of HOS is shown in Fig.
7. IMF 4 and 5 pass both the test of Gaussianity and therefore discarded. The non-Gaussian IMFs of index
higher than 3 are then Thresholded according to the rules discussed in section 3.
The thresholded non-Gaussian IMFs of index higher than 3 are added with the filtered signal to reconstruct
the ECG signals.
5. De-Noising Corrupted ECG Signals By EMD With Application Of HOS
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V. Result and Discussion
The performance measures are as following:
(a) Improvement in Signal to Noise Ratio, 𝑆𝑁𝑅𝑖𝑚𝑝 = 10 log10
𝑦 𝑛 − 𝑥[𝑛] 2𝑁
𝑛=1
𝑥 𝑛 − 𝑥[𝑛] 2𝑁
𝑛=1
Where, x[n] denotes the original ECG signal,
y[n] denotes the noisy ECG signal, and
𝑥 𝑛 denotes the reconstructed de-noised ECG signal
(b) Percent Root Mean Square Difference, 𝑃𝑅𝐷 =
(𝑥 𝑛 − 𝑥[𝑛])2𝑁
𝑛=1
𝑥2[𝑛]𝑁
𝑛=1
× 100
The mean values of the measures for different values of input SNR, for both soft and hard-thresholding
definition for all 12 leads are shown in the table.
The results show that the output SNR of the ECG signals is reasonably improved by de-noising by
implementing the developed algorithm. The PRD is also decreased to a satisfactory value, which reveals the
applicability of the algorithm in real-world environment.
VI. Conclusion
EMD is a very effective method to decompose non-stationary signals. However, when it comes to de-
noise ECG signal, direct de-noising can’t be done because it degrades the quality of the signal. In this paper, an
algorithm is developed to de-noise ECG signal using EMD along with HOS. The algorithm has been proved to
be quite good-performing. It is required to implement the algorithms for signals in noisy environment of the real
world. If the developed algorithm can de-noise the noisy signals successfully enough, then the algorithm can be
used in micro-controller chips to use the technique in ECG recorder machines through integrated circuits (ICs).
8. Figures and Table
Fig 1: original uncorrupted signal Fig 2: signal corrupted by noise with SNR 10 db
Fig 3: Noisy Signal Decomposed Into Imfs
(a) (b)
Fig. 4: direct ECG de-noising (a) removing IMF1 (b) removing IMF1 and IMF2
6. De-Noising Corrupted ECG Signals By EMD With Application Of HOS
www.iosrjournals.org 52 | Page
Fig 5: uncorrupted, noisy ECG and d(n) Fig 6: QRS complex preserved by
Windowing on d[n]
(a) (b)
Fig 7: application of HOS (a) Kurtosis (b) Bispectrum
Table: Results
Input
SNR
𝑺𝑵𝑹𝒊𝒎𝒑 for
Soft
𝑺𝑵𝑹𝒊𝒎𝒑
for Hard
PRD for
Soft
PRD for
Hard
5 db 3.3308 2.9626 3.9073 4.3065
10 db 5.2309 3.877 2.6595 3.2624
15 db 10.7506 7.7349 2.8792 3.2624
20 db 15.5209 12.6355 2.7441 3.0307
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