AU2021104217A4 - A system and method for optimum wavelet basis function selection for ecg arrhythmia denoising using artificial intelligence - Google Patents
A system and method for optimum wavelet basis function selection for ecg arrhythmia denoising using artificial intelligence Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 46
- 206010003119 arrhythmia Diseases 0.000 title claims abstract description 17
- 230000006793 arrhythmia Effects 0.000 title claims abstract description 17
- 238000013473 artificial intelligence Methods 0.000 title description 2
- 238000013528 artificial neural network Methods 0.000 claims abstract description 11
- 238000012549 training Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 238000011156 evaluation Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 description 18
- 230000008901 benefit Effects 0.000 description 9
- 238000000354 decomposition reaction Methods 0.000 description 5
- 238000013459 approach Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
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- 230000000747 cardiac effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
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- 230000029058 respiratory gaseous exchange Effects 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/148—Wavelet transforms
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30048—Heart; Cardiac
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
Abstract
The present disclosure relates to a system and method for optimum
wavelet basis function selection for ECG arrhythmia denoising comprises
optimizing wavelet transform to remove noise from ECG signals by
dividing the signal at a certain de-composition level upon producing
approximation and detailed coefficients; and designing an artificial neural
network-based classifier and using wavelet transform based extracted
ECG feature sets as input of the classifier to classify in two categories of
matched/un-matched of the target output.
18
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Description
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The present disclosure relates to a system and method for optimum wavelet basis function selection for ECG arrhythmia denoising.
Electrocardiogram (ECG) is the most used tool for diagnosing cardiac problems. It uses different ECG electrodes on the body surface to capture the electrical activity of the heart. This recorded ECG, on the other hand, is an extremely low frequency signal with a bandwidth of 0.05Hz to 120Hz. It is frequently corrupted by a variety of interfering sounds, which affect its features and qualities, making it difficult for medical professionals to understand. Baseline Wander Noise (BLW) and Power Line Interference Noise (PLIN) are the two most prevalent forms of noise (PLI). The noise is caused by the patient's body movement when breathing, coughing, or coughing during the ECG examination, as well as power supply fluctuations. The initial stage of ECG signal analysis, known as pre-processing, is used to generate a noise-free signal.
Wavelet-based methods have been utilised for ECG signal denoising over the last decade. It entails deciding on a wavelet basis function, signal decomposition level, and thresholding approach. The performance of DWT-based thresholding techniques is evaluated using numerical simulation of statistical parameters.
However, based on the findings, it can be concluded that there is no efficient approach for selecting a wavelet function for noise content reduction.
In the view of the forgoing discussion, it is clearly portrayed that there is a need to have a system and method for optimum wavelet basis function selection for ECG arrhythmia denoising.
The present disclosure seeks to provide an optimum selection method and a wavelet basis function system for elimination of noise from ECG arrhythmia signals.
In an embodiment, a system for optimum wavelet basis function selection for ECG arrhythmia denoising is disclosed. The system includes an optimization unit for optimizing wavelet transform for removing noise from ECG signals by dividing the signal at a certain de-composition level upon producing approximation and detailed coefficients. The system further includes an artificial neural network-based classifier for receiving wavelet transform based extracted ECG feature sets to classify in two categories of matched/un-matched of the target output.
In another embodiment, a method for optimum wavelet basis function selection for ECG arrhythmia denoising is disclosed. The method includes optimizing wavelet transform to remove noise from ECG signals by dividing the signal at a certain de-composition level upon producing approximation and detailed coefficients. The method further includes designing an artificial neural network-based classifier and using wavelet transform based extracted ECG feature sets as input of the classifier to classify in two categories of matched/un-matched of the target output.
In an embodiment, dominant frequency of baseline wander noise (BLW) and power line interference noise (PLI) is corresponding to approximation coefficient and detail coefficients.
In an embodiment, formulated wavelet based denoising comprises eliminating approximation coefficient and detail coefficients to remove low and high frequency noise components; employing universal thresholding to measure noise level, wherein the noise level is standard deviation of noise in each frequency sub-bands; determining modified values of detail coefficients at every level using soft thresholding; and reconstructing de noised signal using approximation coefficients and modified values of detail coefficients.
In an embodiment, the standard deviation is estimated by a median absolute deviation (MAD).
In an embodiment, statistical parameters are used to evaluate performance of denoising method, wherein the statistical parameters are standard deviation and mean square error (MSE), which gives variation of noise through the signal.
In an embodiment, each ECG signals are band-pass filtered at 0.1 100Hz and then sampled at 360 Hz which is further used for evaluation and performance assessment of process to detect realistic clinical significances.
In an embodiment, denoising of ECG signals comprises applying DWT at level 9; extracting approximation coefficient and detail coefficients followed by applying threshold on remaining coefficients; and applying IDWT for reconstructing denoised ECG signal and extracting statistical parameters for removal of Baseline Wander and Power line interference noise.
In an embodiment, steps for training and classifying signals in two categories comprises obtaining input feature sets from denoised wavelet coefficients; and training MLP neural network for classifying signals in two categories of matched/un-matched of the target output.
In an embodiment, wavelet transform based thresholding method is employed to eliminate and shrinkage noisy wavelet coefficients.
An object of the present disclosure is to develop an optimum selection and a wavelet basis function system to eliminate noise from ECG arrhythmia signals.
Another object of the present disclosure is to assess the denoising effectiveness of various wavelet basis functions using a constructed classifier's selectivity, sensitivity, and accuracy.
Another object of the present disclosure is to find out most suitable wavelet basis function for denoising of six types of ECG arrhythmias.
Yet another object of the present invention is to deliver an expeditious and cost-effective method for optimum wavelet basis function selection for ECG arrhythmia denoising.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a block diagram of a system for optimum wavelet basis function selection for ECG arrhythmia denoising in accordance with an embodiment of the present disclosure; Figure 2 illustrates a flow chart of a method for optimum wavelet basis function selection for ECG arrhythmia denoising in accordance with an embodiment of the present disclosure; Figure 3 illustrates denoising model in accordance with an embodiment of the present disclosure; Figure 4 illustrates performance of classifier in accordance with an embodiment of the present disclosure; and Figure 5 illustrates Table 1 depicts frequency sub-bands if sampling frequency is 360Hz.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
Referring to Figure 1, illustrates a block diagram of a system for optimum wavelet basis function selection for ECG arrhythmia denoising is illustrated in accordance with an embodiment of the present disclosure. The system 100 includes an optimization unit 102 is configured for optimizing wavelet transform for removing noise from ECG signals by dividing the signal at a certain de-composition level upon producing approximation and detailed coefficients.
In an embodiment, an artificial neural network-based classifier 104 is configured for receiving wavelet transform based extracted ECG feature sets to classify in two categories of matched/un-matched of the target output.
In an embodiment, a processing unit 106 is configured with the optimization unit 102 and classifier 104 for analyzing and thereby controlling operation of the optimization unit 102 and classifier 104 according to threshold values assigned by a user.
In an embodiment, the ECG wave patterns are recognized and analyzed using modern machine learning techniques in combination with computer assisted design. When compared to interpretation conducted by unaided hand and naked sight, computerized analysis of the ECG signal aids the cardiologist in properly diagnosing cardiac illness. As a consequence, using machine learning approaches shows to be more adaptable and enhances filtering outcomes.
Figure 2 illustrates a flow chart of a method for optimum wavelet basis function selection for ECG arrhythmia denoising in accordance with an embodiment of the present disclosure. At step 202, the method 200 includes optimizing wavelet transform to remove noise from ECG signals by dividing the signal at a certain de-composition level upon producing approximation and detailed coefficients.
At step 204, the method 200 includes designing an artificial neural network-based classifier and using wavelet transform based extracted ECG feature sets as input of the classifier to classify in two categories of matched/un-matched of the target output.
In an embodiment, dominant frequency of baseline wander noise (BLW) and power line interference noise (PLI) is corresponding to approximation coefficient and detail coefficients.
In an embodiment, formulated wavelet based denoising includes eliminating approximation coefficient and detail coefficients to remove low and high frequency noise components. Then, employing universal thresholding to measure noise level, wherein the noise level is standard deviation of noise in each frequency sub-bands. Then, determining modified values of detail coefficients at every level using soft thresholding. Thereafter reconstructing de-noised signal using approximation coefficients and modified values of detail coefficients.
In an embodiment, the standard deviation is estimated by a median absolute deviation (MAD).
In an embodiment, statistical parameters are used to evaluate performance of denoising method. The statistical parameters are standard deviation and mean square error (MSE), which gives variation of noise through the signal.
In an embodiment, each ECG signals are band-pass filtered at 0.1 100Hz and then sampled at 360 Hz which is further used for evaluation and performance assessment of process to detect realistic clinical significances.
In an embodiment, denoising of ECG signals includes applying DWT at level 9; extracting approximation coefficient and detail coefficients followed by applying threshold on remaining coefficients. Then, applying IDWT for reconstructing denoised ECG signal and extracting statistical parameters for removal of Baseline Wander and Power line interference noise.
In an embodiment, steps for training and classifying signals in two categories includes obtaining input feature sets from denoised wavelet coefficients. Then, training MLP neural network for classifying signals in two categories of matched/un-matched of the target output.
In an embodiment, wavelet transform based thresholding method is employed to eliminate and shrinkage noisy wavelet coefficients.
Using cascaded low pass and high pass filters, Wavelet decomposed the original signal into multiple frequency bands. If Fs is sampling frequency, Fjis range of frequency, then each wavelet sub-bands having frequencies components calculated using following relation:
F !AFj) 2-JFs (1)
The frequency contents for approximation and detail coefficients, results from low pass and high pass- filtering, are find in the interval
[0,>]and [ , ] , respectively. Due to filters leakage effects, frequencies overlapping is obtained. Therefore, in realizable systems, frequency corresponds to approximation coefficients are higher than and detail coefficients are lower than but higher than[ ]. Table 1, shows frequency distribution in each sub-band up to level 9:
It concludes from above table that application of wavelet transform divide the signal, at a certain de-composition level 9, it produces approximation and detailed coefficients. The dominant frequency of BLW and PLI is corresponding to approximation coefficient agand detail coefficients d 3, respectively. Thus, the Lth level DWT results in L-detailed coefficients (di to dL, iEI) and Lth approximation coefficient (aL). If fmax5S the maximum input signal frequency and fc is the cut-off frequency, then L can be calculated using eq. 2,
L =int log f, (2)
Here, f- 180Hz and corresponds to dominant noise frequency given by:
c 0.5HzforBLW() f50/60HzforPLI]
Thus, from eq. 2 & 3, maximum number of decomposition level to select PLI and BLW noise frequency sub-bands are 3 and 9, respectively. Thus, we use decomposition level 9, as it provides all frequency sub bands of level 3 decomposition also. Therefore, the formulated wavelet based denoising procedure has following steps:
Shrinkage of Wavelet Coefficients: Elimination of ag and d 3 is performed to remove low and high frequency noise components, respectively.
Modified rest of Wavelet detail Coefficients: Universal thresholding is used to measure noise level(o;(noise)), say, standard deviation of noise in each frequency sub-bands (j=1 to L) as given by:
=oj 2og 2 N (4) Here, oj(noise)is estimated by using median absolute deviation (MAD), given by |MAD|/0.6745.
The next step is to determined modified values of detail coefficients at every level, say, jth level using soft thresholding, given by:
dT1 (t) =sgn (dTj(t)) (lxi- A); dT(t)>A, (5) d~ j~t) = 0; d Tj (t) :! Aj 5
Thus, adaptive threshold rule on noisy wavelet coefficients (wk)at each level, is defined as
dTj(Ajwk) (x(wk)ifwk w0; > '(6) ifIWkI < A6
The final step, is reconstruction of de-noised signal xd(t), using jth approximation coefficients and modified values of detail coefficients upto j" level from dTitodT(j=1,2 ........ 9, an integer value). To evaluate performance of denoising method, statistical parameters, (1) Standard
Deviation(c-),and (2)Mean Square Error (MSE) is used, which gives variation of noise through the signal. If x'(i), x(i) and s(i) are de-noised signal, original signal and noisy signal, respectively, then numericalsimulation of Standard Deviation (a)gives variation of noise through the signal, and given by:
o- = (7), &
MSE 1 (x(i)- (i))2(8)
Least value of standard deviation is desirable.
Figure 3 illustrates denoising model in accordance with an embodiment of the present disclosure. At decomposition level 9, DWT is used to the downloaded ECG data to obtain noisy wavelet coefficients. The main noise frequencies are extracted and shrunk using the relation in eq 3, and the rest of the wavelet coefficients are changed using the threshold criteria in Eqs 4 and 6. The classifier uses the collected de noised wavelet coefficients as an input to classify matched/unmatched results with the target output. As a result, the suggested technique is divided into two steps, as depicted in Figure 3.
Figure 4 illustrates performance of classifier in accordance with an embodiment of the present disclosure. It is a supervised learning approach that uses back propagation (Levenberg-Marquarlt) and the feed forward technique to produce and train data in a specified network. In general, MLP has three layers of nodes: (I) the input layer, which represents the input features set, (II) the hidden layer, and (III) the output layer, which corresponds to the classes to be categorized. For function fitting, a non-linear activation sigmoid transfer function is used in the hidden layer, while a linear transfer function is used in the output layer. The network is modified based on (i) its error, referred to as the training phase; (ii) and (iii) its independent and generalized network performance evaluation, referred to as the testing and validation phases, respectively. In a feed forward neural network, 70 percent of ECG data are utilized for training, 15 percent for testing, and 15 percent for validation. In the NN classifier, wavelet transform-based extracted ECG feature sets are utilized as inputs to categorize the results into two categories: matched/un-matched of the target output.
The original ECG arrhythmias signals are acquired from an open source database for testing purposes. These signals are utilized to show that the heart isn't working properly.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
Claims (10)
1. A method for optimum wavelet basis function selection for ECG arrhythmia denoising, the method comprises:
optimizing wavelet transform to remove noise from ECG signals by dividing the signal at a certain de-composition level upon producing approximation and detailed coefficients; and designing an artificial neural network-based classifier and using wavelet transform based extracted ECG feature sets as input of the classifier to classify in two categories of matched/un-matched of the target output.
2. The method as claimed in claim 1, wherein dominant frequency of baseline wander noise (BLW) and power line interference noise (PLI) is corresponding to approximation coefficient and detail coefficients.
3. The method as claimed in claim 1, wherein formulated wavelet based denoising comprises:
eliminating approximation coefficient and detail coefficients to remove low and high frequency noise components; employing universal thresholding to measure noise level, wherein the noise level is standard deviation of noise in each frequency sub bands; determining modified values of detail coefficients at every level using soft thresholding; and reconstructing de-noised signal using approximation coefficients and modified values of detail coefficients.
4. The method as claimed in claim 3, wherein the standard deviation is estimated by a median absolute deviation (MAD).
5. The method as claimed in claim 3, wherein statistical parameters are used to evaluate performance of denoising method, wherein the statistical parameters are standard deviation and mean square error (MSE), which gives variation of noise through the signal.
6. The method as claimed in claim 3, wherein each ECG signals are band-pass filtered at 0.1-100Hz and then sampled at 360 Hz which is further used for evaluation and performance assessment of process to detect realistic clinical significances.
7. The method as claimed in claim 5, wherein denoising of ECG signals comprises:
applying DWT at level 9; extracting approximation coefficient and detail coefficients followed by applying threshold on remaining coefficients; and applying IDWT for reconstructing denoised ECG signal and extracting statistical parameters for removal of Baseline Wander and Power line interference noise.
8. The method as claimed in claim 5, wherein steps for training and classifying signals in two categories comprises:
obtaining input feature sets from denoised wavelet coefficients; and training MLP neural network for classifying signals in two categories of matched/un-matched of the target output.
9. The method as claimed in claim 7, wherein wavelet transform based thresholding method is employed to eliminate and shrinkage noisy waveletcoefficients.
10. A system for optimum wavelet basis function selection for ECG arrhythmia denoising, the system comprises:
an optimization unit for optimizing wavelet transform for removing noise from ECG signals by dividing the signal at a certain de-composition level upon producing approximation and detailed coefficients; and an artificial neural network-based classifier for receiving wavelet transform based extracted ECG feature sets to classify in two categories of matched/un-matched of the target output.
Figure 2
Figure 3
Figure 4
Figure 5
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