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Electroencephalogram (EEG) is a widely used signal for analyzing the activities of brain. It has extensively used for the diagnosis of different nervous system disorders such as Alzheimer's, Parkinson's, Seizures, Epilepsy, etc. Ocular activity creates significant artifacts in electroencephalogram recordings. These artifacts increase complexity in analyzing the EEG and obtaining the clinical information...
This paper introduces the application of the discrete wavelet transform in noise reduction of real uterine Electromyography (EMG) signals. With the appropriate choice of the wavelet function and the selection of the suitable decomposition level, it's possible to remove interference noise effectively. Signal to Noise Ratio (SNR) values are calculated to evaluate the global performance of noise reduction...
This paper presents a simple and novel approach for de-noising of the Audio Signals i.e. non-stationary signal using statistical distribution function at different sub-band level of coefficients. The performance of wavelets are analysed under various thresholding techniques. Nonstationary signals are continuous in nature consequently we use 1D Discrete Wavelet Transform which gives us a better time-...
Electrocardiogram (ECG) signals usually corrupted by different types of noise like power line interference, baseline drift due to respiration, electromyogram interference, abrupt baseline shift and their composite noise. Denoising of noisy signal has great clinical importance for the diagnosis of cardiac abnormalities. In this paper, dual tree complex wavelet transform (DTCWT) has been used to denoise...
Human activities are recognized from the Electrooculogram (EOG) signal generated from the movement of eye. Hence early, accurate preprocessing of EOG signals is important. In recent years, this became an active area of research. The EOG signal captured using acquisition device is corrupted with the noise and device intrinsic, thus pre processing (noise reduction) is first and foremost step in any...
In this paper, Denoising of ECG signal using thresholding criteria and wavelet decompositions. A modified threshold criteria proposed in the paper of Denoising. Here, an optimal wavelet selection are illustrated using signal retained energy (RE), percentage root mean square difference (PRD), signal-to-noise ratio (SNR) and mean square error (MSE) parameters. ECG signal contained the noise due to interference...
Identification of optimal denoising algorithm for ECG signals using DWT is described. The parameters are determined by comparing different types of wavelet functions, decomposition levels and threshold selection methods. The algorithm is used to improve the SNR of the ECG contaminated by disturbances like power line interferences, to present a clean signal for accurate auto diagnosis.
The ECG signal is used for various medical diagnoses. For diagnosis purposes, the ECG signal must be free from the noise and undesired disturbances. In the present work the best algorithm for de-noising the ECG signal is identified using Discrete Wavelet Transform. Statistical analysis and comparison has been made to find the best wavelet function, their decomposition level and threshold selection...
The aim of this study was to investigate and select the wavelet function that is optimum to denoise the surface electromyography (sEMG) signal for multifunction myoelectric control. Wavelet denoising algorithm has been used to find the optimal wavelet function for removing white Gaussian noise (WGN) at various signal-to-noise ratios (SNRs) from sEMG signals. A total of 53 wavelet functions were used...
To obtain a high robust of speech recognition for noisy conditions, a new pre-processing stage based on wavelet thresholding algorithm is proposed in this paper. The purpose of using the DWT is to benefit from its localization property in the time and frequency domains. Compromise function is proposed compared with hard and soft thresholding function. A new thresholding value, Neyman-Pearson criterion...
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