Subband-Adaptive Shrinkage for Denoising of ECG Signals

Poornachandra, S.; Kumaravel, N.

doi:10.1155/asp/2006/81236

Cited by 15 publications

(9 citation statements)

References 6 publications

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“…An illustration of the proposed scanning technique, using a threshold value of 3, is shown in By selecting a nonnegative threshold, the small approximation coefficients can be reset to zeros, resulting in a vector of approximation coefficients consisting of mostly zeros. A thorough description of thresholding methods can be found in [32][33]. The resulting thresholded vector is then encoded by the proposed system, using a modified version of the run-length encoding (RLE) scheme.…”

Section: Wavelet Transform and Optimum Threshold Levelmentioning

confidence: 99%

Detection of COVID-19 Cases In Chest X-ray Images Using Wavelets And Support Vector Machines

Sarhan

2020

Preprint

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This paper presents a novel technique for the detection of COVID-19 cases in chest X-ray scans. The proposed system uses the Wavelet transform (WT) to find discriminative features in the X-ray images and Support Vector Machines (SVM) to classify the extracted features. The WT is well-known for its energy compression capability. The proposed system preprocesses the chest X-ray image with WT which produces a set of approximation coefficients that include a limited number of high-magnitude coefficients. The proposed system introduces a novel threshold scanning technique that extracts only selected high-energy approximation coefficients. The thresholded coefficients are encoded using a modified version of the run-length encoding (RLE) scheme. The resulting code vector is used as the features representing the input image. These limited features are then presented to a SVM for classification (normal or COVID-19). The use of limited features in the proposed system significantly simplifies the work of the SVM classifier and increases its accuracy. Another advantage of diminishing the number of features is the reduction of the time complexity of the system. The proposed system produces a maximum accuracy of 94.5% when the decomposition level is 2 and the threshold level is 903.

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Section: Wavelet Transform and Optimum Threshold Levelmentioning

confidence: 99%

Detection of COVID-19 Cases In Chest X-ray Images Using Wavelets And Support Vector Machines

Sarhan

2020

Preprint

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“…The subband adaptive shrinkage exhibits two characteristics: antisymmetric exponential characteristics and symmetry characteristics [6]. Due to the characteristics, it will keep significant coefficients, which represent signal characteristics, and shrink the remaining wavelet coefficients exponentially towards zero.…”

Section: (A) (B) Fig 3 Shrinkage Functions (A) Hyper Trim (B) Subbanmentioning

confidence: 99%

“…Poornachandra and N. Kumaravel suggested subband adaptive shrinkage function [6]. It is a nonlinear model and works on hyperbolic function which will better than the soft shrinkage function.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of various wavelet shrinkage functions for ECG signals

Selvakumarasamy

Chandra²,

Amutha³

2014

2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)

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ECG is the generally recognized biomedical signal for diagnostic purpose and it shows the electrical activity of the heart muscles. ECG is a non stationary signal where wavelet transform (WT) is useful for analyzing it. Denoising is the process of removing noise in order to preserve useful information. A comparative analysis of various wavelet shrinkage functions for ECG signal denoising such as soft and hard shrinkage functions, hyper shrinkage function, subband -adaptive shrinkage functions are discussed. The performance of the wavelet shrinkage functions are compared based on the signal to noise ratio (SNR) Value.

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“…Hyper trim shrinkage is the one which tends to reduce the minimum mean square error between the original and denoised ECG signal. Subband adaptive shrinkage function is proposed by S. Poornachandra and N. Kumaravel 5 . It is a nonlinear model and works on hyperbolic function which resembles the basic shrinkage distribution.…”

Section: Introductionmentioning

confidence: 99%

Subband Adaptive Shrinkage Function Using Fuzzy Logic

Selvakumarasamy¹,

Poornachandra²,

Amutha³

2015

Biomed. Pharmacol. J.

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ECG is the electrical activity of the heart which uses for the various diagnostic purposes. Denoising is a process which helps to remove the noise from the original signal. Wavelet transform is suitable for non stationary signal such as ECG signal, EEG signal, PPG signal etc. The wavelet coefficients obtained by wavelet transform are made zero which in turns reduces the noise to zero level. Fuzzy logic is a structured, powerful problem solving technique that approximates a function through linguistic variables. A fuzzy membership function is an abstract which each point in the input variable is mapped to a membership value between 0 and 1.In this paper, noise is removed from an ECG signal by using subband adaptive shrinkage function using the fuzzy logic. The threshold value is selected by the fuzzy membership function for signal denoising. Among the other shrinkages functions, subband adaptive method is preferred as it produces good result by holding linearity at discontinuities. The parameters used for measuring the performance are signal to noise ratio (SNR), percentage root mean square difference (PRD), and mean square error (MSE).

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Subband-Adaptive Shrinkage for Denoising of ECG Signals

Cited by 15 publications

References 6 publications

Detection of COVID-19 Cases In Chest X-ray Images Using Wavelets And Support Vector Machines

Detection of COVID-19 Cases In Chest X-ray Images Using Wavelets And Support Vector Machines

A comparative analysis of various wavelet shrinkage functions for ECG signals

Subband Adaptive Shrinkage Function Using Fuzzy Logic

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