Electrocardiogram (EKG or ECG) is an important electrical activity of the human Heart. ECG is used for the primary diagnosis of heart diseases since it shows the electrophysiology of the heart and the ischemic changes that may occur like the myocardial infarction, conduction defects, and arrhythmia. But, in real condition, ECG is often corrupted by different artifacts and noises. For the purpose of quality diagnosis, the ECG signal must be clearly de-noised to remove all noises and artifacts from the signal. In this paper, we present the Wavelet Transform, a new approach in digital signal processing to filter the ECG signal. Different ECG signals from M IT/BIH arrhythmia database are used with added 10dB, 5dB & 0dB Power Line Interference (PLI) noise which is common in ECG signal. The results were evaluated using M ATLAB software. Basically, two synthesis parameters M ean Square Error (M SE) and Signal to Noise ratio (SNR) have been used. The prime aim of this paper is to adapt the discrete wavelet transform (DWT) to improve the (ECG) signal quality for better clinical diagnosis. The evaluated results have been compared with Butterworth IIR filter. The proposed method shows improvement in output SNRo for 5dB noise is 98.5% and for 10dB noise is 95.7%.
This chapter deals with performance analysis of CUDA implementation of an image quality assessment tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results that would have been obtained with subjective testing. For objective image analysis, this new technique represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR. The method is computationally intensive and this poses issues in places wherever real time quality assessment is desired. We tend to develop a CUDA implementation of this technique that offers a speedup of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.
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