Loading...
| Friend's email | |
| Your name | |
| Your email | |
| enter code | |
This page was sent successfuly
- Type of Document: M.Sc. Thesis
- Language: English
- Document No: 46502 (55)
- University: Sharif University of Technology, International Campus, Kish Island
- Department: Science and Engineering
- Advisor(s): Shamsollahi, Mohammad Bagher
- Abstract:
- The goal of the research presented in this thesis is removing noise from electrocardiogram (ECG) signals. The electrocardiogram is a test that measures the electrical activity of the heart. The information obtained from an electrocardiogram can be used to diagnose different types of heart disease. It may be useful for seeing how well the patient is responding to treatment. The extraction of high resolution ECG signals from noisy measurements is among the most tempting open problems of biomedical signal processing. Extracting useful clinical information from the real (noisy) ECG requires reliable signal processing techniques. Numerous methods have been reported to denoise ECG signals based on filter banks, principal component analysis (PCA), independent component analysis (ICA), neural networks (NNs), adaptive filtering and wavelet transform. These methods have some drawbacks. They remove not only noise but also the high frequency components of non-stationary signals. In the worse they can remove the characteristic points of signals that are crucial for successful detection of waveform.
In this thesis we use the Empirical Mode Decomposition (EMD) method which is a new signal processing approach that has been proposed in recent years. The EMD is invented to overcome the limitation of Wavelet Transform (WT) or Fourier Transform (FT) based methods. The basis function which is needed in EMD is signal itself, unlike the WT where the basis function is fixed. The EMD does not use any predetermined filters or transforms, and it does not need to assume data is linear and stationary like FT. This is one reason that motivates us to use EMD. Further another interest of the EMD is that no assumptions concerning the linearity or the stationary are made about the signal to be analyzed. The extraction of IMFs is nonlinear, but their linear recombination is accurate. The EMD decomposes any time-series signals into the sum of a finite number of Intrinsic Mode Function (IMFs) components according to different time scale. In this thesis we explain the principle of EMD and the limitation of it. The different EMD based denoising methods are studied and some of them are implemented for ECG signals denoising. The basic idea of EMD denoising is to detect the noisy IMFs or noisy parts of IMFs and then filter or remove them. Therefore, the denoised signal is obtained by adding the filtered IMFs which are the output of filtering operations to the remaining non-filtered IMFs. The combination of different EMD based denoising methods are used to find a new method that can remove noise from ECG signals with minimum loss of useful information of original signals.
The different EMD based denoising methods and the proposed method are applied on ECG signals available in MIT–BIH database with additive white Gaussian and real Electromyography (EMG) noise. The quantitative results of our proposed method, the EMD based denoising methods, Butterworth filter and Savitzky-Golay filter are compared and the results show that the proposed method has better performance - Keywords:
- Electrocardiogram ; Bidimentional Empirical Modes Decomposition ; Denoising ; Intrinsic Mode Function ; Thresholding
Digital Object List-
محتواي کتاب - view
Bookmark