Abstract:In recent years, ECG signal plays an important role in the primary diagnosis, prognosis and survival analysis of heart diseases. In this paper a new approach based on the threshold value of ECG signal determination is proposed using Wavelet Transform coefficients. Electrocardiography has had a profound influence on the practice of medicine. The electrocardiogram signal contains an important amount of information that can be exploited in different manners. The ECG signal allows for the analysis of anatomic and physiologic aspects of the whole cardiac muscle. Different ECG signals are used to verify the proposed method using MATLAB software. Method presented in this paper is compared with the Donoho's method for signal denoising meanwhile better results are obtained for ECG signals by the proposed algorithm.

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Abstract:In this research, methods to detect and evaluate the main parameters of the ECG signal are presented in order to improve its clinical properties. Two techniques are addressed in this research, Piecewise method and the Analytical Approach. These techniques are used to determine such as: percent (rms) difference (PRD), signal-noise-ratio (SNR) and the compression ratio (CR) which are used to evaluate the quality of ECG signals. The evaluation and processing of the methodology quality and the metrological point of view of ECG signal problems are subsequently obtained from detection and filtration. This technique has been successfully implemented to determine the legal accuracy rang of ECG signal for detection, filtration and compression.

Focus of the method was to present image identification and labeling using new combination of dynamic transforms. This method is based on using the combination of the Discrete Multi-wavelet Transform (DMWT) and Wavenet Transform (WN). In this method, the resulting coefficients were computed by the proposed multi-wavelets transform for single-level decomposition. The low pass sub bands of the upper left corner are considered in the proposed method as a resemblance and a smaller version of the original image. The Wavenet Transform (WN) of low -low coefficients will be obtained one after another and the outcome from the band pass sub bands of the lower right corner is the feature extraction of each image to the recognition identification of the image. This method gave an excellent result: 99% for a database of 100 different images which indicate that the suggested algorithm is an excellent tool to process the database of standard pose of image. The algorithm is implemented using MATLAB programming languages version 7.

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