ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm

2012 ◽  
pp. 199-205
Author(s):  
Alka Gautam ◽  
Hoon-Jae Lee ◽  
Wan-Young Chung
Keyword(s):  
Signal To Noise Ratio ◽  
Random Noise ◽  
Noise Cancellation ◽  
Peak Detection ◽  
Lms Algorithm ◽  
Ecg Signal ◽  
Maximum Point ◽  
Signal To Noise ◽  
Filtering Algorithm ◽  
Overall Performance

In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.

ECG Signal De-noising with Asynchronous Averaging and Filtering Algorithm

2010 ◽  
Vol 5 (2) ◽  
pp. 30-36 ◽  
Author(s):  
Alka Gautam ◽  
Hoon-Jae Lee ◽  
Wan-Young Chung
Keyword(s):  
Signal To Noise Ratio ◽  
Random Noise ◽  
Peak Detection ◽  
Lms Algorithm ◽  
Ecg Signal ◽  
Maximum Point ◽  
Signal To Noise ◽  
Signal Shape ◽  
Filtering Algorithm ◽  
Overall Performance

In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.

The Adaptive Filtering Algorithms for Engine’s Noise Cancellation in Speech Signals

2011 ◽  
Vol 130-134 ◽  
pp. 1323-1326
Author(s):  
Xiu Ying Zhao ◽  
Hong Yu Wang ◽  
De You Fu ◽  
Hai Shen Zhou
Keyword(s):  
Adaptive Filtering ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Speech Signals ◽  
Adaptive Noise Cancellation ◽  
Signal To Noise ◽  
Least Mean Squares ◽  
Signal Noise ◽  
Filtering Algorithm ◽  
Adaptive Noise

The presence of noise superimposed on a signal limits the receiver’s ability to correctly identify the intended signal. The principal of adaptive noise cancellation is to acquire an estimation of the unwanted interfering signal and subtract it from the corrupted signal. Noise cancellation operation is controlled adaptively with the target of achieving improved signal to noise ratio. This paper describes the Least Mean Squares (LMS) adaptive filtering algorithm. The algorithm was implemented in Matlab and was tested for noise cancellation in speech signals.

Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. V229-V237 ◽  
Author(s):  
Hongbo Lin ◽  
Yue Li ◽  
Baojun Yang ◽  
Haitao Ma
Keyword(s):  
Weight Function ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Signal To Noise ◽  
Transform Method ◽  
Time Frequency ◽  
Frequency Peak ◽  
Noise Ratio

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

Perception of Movement and Correlation in Stroboscopically Presented Noise Patterns

Perception ◽  
1985 ◽  
Vol 14 (2) ◽  
pp. 209-224 ◽  
Author(s):  
Andrea J van Doorn ◽  
Jan J Koenderink ◽  
Wim A van de Grind
Keyword(s):  
Visual Field ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Temporal Correlation ◽  
Motion Aftereffect ◽  
Luminance Contrast ◽  
Signal To Noise ◽  
Field Evidence ◽  
Spatiotemporal Integration ◽  
Moving Pattern

The detection of spatiotemporal correlation in visual displays has been studied with stroboscopically presented random-noise patterns and with a signal-to-noise ratio paradigm in which the moving pattern was masked with spatiotemporal white noise. These methods reveal the ability of the visual system to detect correlation of spatiotemporal structures, rather than luminance contrast. The effects of stroboscopic rate, exposure duration, target size, and the extent of discrete spatial shifts were studied in both the central and the peripheral visual field. Evidence for orientation-selective and speed-selective mechanisms was found, as well as for extensive spatiotemporal integration. Bounds on parameters of spatial and temporal correlation and integration were obtained. The results are similar to those reported earlier, and also extend them. Their relation to results obtained through other paradigms (eg the motion aftereffect) is explored.

Stacking seismic data using local correlation

Geophysics ◽  
2009 ◽  
Vol 74 (3) ◽  
pp. V43-V48 ◽  
Author(s):  
Guochang Liu ◽  
Sergey Fomel ◽  
Long Jin ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Image Point ◽  
Local Correlation ◽  
Seismic Profiles ◽  
Signal To Noise ◽  
Prestack Migration

Stacking plays an important role in improving signal-to-noise ratio and imaging quality of seismic data. However, for low-fold-coverage seismic profiles, the result of conventional stacking is not always satisfactory. To address this problem, we have developed a method of stacking in which we use local correlation as a weight for stacking common-midpoint gathers after NMO processing or common-image-point gathers after prestack migration. Application of the method to synthetic and field data showed that stacking using local correlation can be more effective in suppressing random noise and artifacts than other stacking methods.

Distributed Optical Fiber Raman Signal Noise Cancellation Based on Empirical Mode Decomposition

2012 ◽  
Vol 198-199 ◽  
pp. 1621-1626 ◽  
Author(s):  
You Ping Zhong ◽  
Qi Zhang ◽  
Di Zhou
Keyword(s):  
Optical Fiber ◽  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Raman Signal ◽  
Signal To Noise ◽  
Mode Decomposition ◽  
Improve Measurement ◽  
Distributed Optical Fiber ◽  
Anti Stokes

The distributed optical fiber Raman sensor system was widely used for real-time measurement temperature, but the Anti-Stokes and Stokes scattering Raman signal is very weak. In order to improve measurement accuracy the signal must be denoised before obtaining the temperature. In this paper, a new noise cancellation of empirical mode decomposition is proposed for enhancing signal-to-noise ration of the Anti-Stokes and Stokes scattering Raman signal. The signal-to-noise ratio was enhanced by using this method and it is easy to be realized in computer.

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