http://www.kresttechnology.com/krest-academic-projects/krest-major-projects/ECE/B-Tech%20Papers/67.pdf

ECG signal denoising and baseline wander correction based on the empirical mode decomposition

Electrocardiographic (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy Electrocardiographic (ECG) signal is very motivating challenge. Prior to automated analysis, the noises present in ECG signal need to be eliminated for accurate diagnosis. Many researchers have been reported different methods for denoising the ECG signal in recent years. In this paper, an optimized threshold mechanism is proposed for wavelet based medical signal noise reduction. This scheme is based on a variable step size firefly algorithm (VSSFA) in dual tree complex wavelet scheme, in which the VSSFA is utilized for threshold optimization. This approach is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5dB and 10dB. Simulation result illustrate that the proposed system is well performance in various noise level, and obtains better visual quality compare with other methods.

http://www.inass.org/share/2016093012.pdf

An Efficient Threshold Prediction Scheme for Wavelet Based ECG Signal Noise Reduction Using Variable Step Size Firefly Algorithm

An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks

A new method is presented to denoise 1-D experimental signals using wavelet transforms. Although the state-of-the-art wavelet denoising methods perform better than other denoising methods, they are not very effective for experimental signals. Unlike images and other signals, experimental signals in chemical and biophysical applications, for example, are less tolerant to signal distortion and under-denoising caused by the standard wavelet denoising methods. The new method: 1) provides a method to select the number of decomposition levels to denoise; 2) uses a new formula to calculate noise thresholds that does not require noise estimation; 3) uses separate noise thresholds for positive and negative wavelet coefficients; 4) applies denoising to the approximation component; and 5) allows the flexibility to adjust the noise thresholds. The new method is applied to continuous wave electron spin resonance spectra and it is found that it increases the signal-to-noise ratio (SNR) by more than 32 dB without distorting the signal, whereas standard denoising methods improve the SNR by less than 10 dB and with some distortion. In addition, its computation time is more than six times faster.

A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

Heartbeat classification using projected and dynamic features of ECG signal

Wavelet-based denoising using subband dependent threshold for ECG signals

A novel method for the elimination of power line frequency in ECG signal using hyper shrinkage function

Hyper-trim shrinkage for denoising of ECG signal

This paper describes subband dependent adaptive shrinkage function that generalizes hard and soft shrinkages proposed by Donoho and Johnstone (1994). The proposed new class of shrinkage function has continuous derivative, which has been simulated and tested with normal and abnormal ECG signals with added standard Gaussian noise using MATLAB. The recovered signal is visually pleasant compared with other existing shrinkage functions. The implication of the proposed shrinkage function in denoising and data compression is discussed.

/pdf/subband-adaptive-shrinkage-for-denoising-of-ecg-signals-nqbj0ly3td.pdf

Subband-adaptive shrinkage for denoising of ECG signals

An automated segmentation method for Retinal blood vessel extraction is developed. The method uses Morphological structuring element and basic filters for preprocessing. The preprocessing step enhances and makes the retinal images most suitable for vessel extraction. Thresholding technique is employed to extract the blood vessels from the background image. This method is evaluated on the two publicly available data bases namely STARE and DRIVE database. This method is compared with the matched filter method for segmentation and kirsch template method. Our method gives appreciable blood vessel extraction as compared with other methods.

S. Poornachandra

Papers

Wavelet-based denoising using subband dependent threshold for ECG signals

A novel method for the elimination of power line frequency in ECG signal using hyper shrinkage function

Hyper-trim shrinkage for denoising of ECG signal

Subband-adaptive shrinkage for denoising of ECG signals

Retinal blood vessel segmentation using morphological structuring element and entropy thresholding