Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Wavelet

ECG Arrhythmias Classification by Combined Feature Extraction Method and Neural Network

Khalooq Y. Al Azzawi

Engineering and Technology Journal, 2014, Volume 32, Issue 3, Pages 586-596

Electrocardiogram (ECG) became one of the most crucial tool for heart status diagnosis. Generally, several arrhythmias may appear based on different heart rate or ECG signal morphology variation. In this paper, a novel combined feature extraction method to present ECG arrhythmias is proposed. The combination between Wavelet Packet Transform (WPT) entropies and Power Spectrum Density (PSD) is suggested. For classification, Feed Forward Backpropagation Neural Network (FFBPN) is utilized. The experimental results showed that the proposed method can be beneficial for ECG signal arrhythmias classification. MIT-BIH Arrhythmia Database was used for algorithm testing. The proposed method was compared with three state of art methods, where was of better performance reached about 80%. The proposed method as well as other methods was tested in noisy environment for comparison investigations. The suggested method is promising approach for arrhythmias classification. However, enormous testing data set might significantly improve the results.

Image Compression Based on 2D Dual Tree Complex Wavelet Transform (2D DT-CWT)

Salih Husain Ali; Aymen Dawood Salman

Engineering and Technology Journal, 2010, Volume 28, Issue 7, Pages 1290-1305

By removing the redundant data, the image can be represented in a smaller
number of bits and hence can be compressed. There are many different methods of
image compression. This paper investigates a proposed form of compression based on
2D Dual Tree complex wavelet transform (2D DT-CWT). Many wavelet coefficients
are closed to zero. Thresholding can modify the coefficients to produce more zeros
that are allowed a higher compression rate. The wavelet analysis does not actually
compress a signal. Therefore Huffman coding is used with a signal processed by the
wavelet analysis in order to compress data. Wide range of threshold values is used in
the proposed form. From the results the proposed form give higher rate of
compression and lower RMS error compared with that forms based on Discrete
Wavelet Transform (DWT), Dual Tree Real Wavelet Transform (DT-RWT) and the
well known method based Discrete Cosine Transform (DCT).

Wavelet and Wavelet Packet Analysis For Image Denoising

Aymen Dawood Salman

Engineering and Technology Journal, 2009, Volume 27, Issue 9, Pages 1755-1765

The denoising method based on wavelet or wavelet packet is used widely for image denoising. It is one of the most popular methods that depends on thresholding the wavelet coefficients using the Soft threshold. There are many methods used to get the threshold that is used in denoising image. In this paper, the amplitude of threshold is calculated depending on RMS error in order to get the best threshold related with the image information. The denoising results show that the wavelet packet is better than the wavelet method in analyzing the image coefficients of information

Wavelet & Multiwavelet Lost Block Reconstruction in Noisey Environment

Atheer A. Sabri; Mutaz S. Abdul-Wahab; Waleed A. Mahmoud

Engineering and Technology Journal, 2009, Volume 27, Issue 4, Pages 717-726

In this paper, an algorithm for reconstruction of a completely lost blocks
using the discrete wavelet and multiwavelet transforms are tested under noisey
The algorithms examined in this paper do not require a DC estimation method.
While most of the previuosly reconstruction methods assume that the DC value is
available or a DC estimation is required.
The reconstruction is achieved using the Boundary Interpolation (BI) which is
based on wavelet transform. The algorithm’s performance is further improved
through the modification of the Boundary Interpolation algorithm.
Another algorithm is studied in this paper which is based on the multiwavelet
The effect of adding a Gaussian noise to the image on the performance of
reconstruction of the algorithms mentioned in this paper is studied.