No abstract

Electrocardiogram (ECG) contains lot of information which can be utilized for a mechanism to detect cardiac abnormalities. The ECG signal is too sensitive to various types of noises as it is of low frequency and has weak amplitude, these noises reduce the diagnostic accuracy and may lead to the incorrect decision of the clinician. So, denoising of ECG signal is an essential requirement for an accurate detection of Heart disease. In this paper, a Dual-Tree Complex Wavelet Transform technique (DTCWT) is presented to denoise the noisy ECG signal and to extract the Principal features followed by implementation of Peak Detection Algorithm. The performance is evaluated on the basis of performance metrics and an increase in SNR is achieved using the technique. With the proposed technique, calculated heart rate is in consensus with the gold standard of the various bench mark databases used and accurate heart disease was determined.

The primary output for any health monitoring system that offers telecardiology services is the recovery of the electrocardiographic (ECG) signal from the noised signal. The mechanized investigation of the ECG signal is the most inspiring challenge for accurate detection of cardiac disease. This could be accomplished by eliminating the various noises from the acquired signal. In this paper, a noise reduction approach employing DTCWT is executed on an ECG signal by proposing a noise estimator along with a detailed assessment of the effect of the choice of the threshold value, threshold algorithm and distribution function. The thresholding technique is executed by varying the threshold value ( γ) and its function ( fn) applied to the proposed estimator (α*). The proposed estimator is scaled by 2n factor to study its impact on performance metrics and the nature of the reconstructed signal utilizing different distribution functions. The best combination of threshold function with threshold value selection has been chosen in this work from eight different sets of threshold value selection rules along with six distinct threshold functions. The experimental results show that the proposed noise reduction approach using a universal modified threshold level-dependent threshold with non-negative garrote threshold function for normal distribution with n = 3 delivers 80.72dB SNR with a subsequent reduction in MSE and PRD as compared with other standard techniques. An elaborate empirical analysis for selecting the distribution function for obtaining the best possible threshold function and technique is the prime objective and novelty of this research work.

CORDIC algorithm is used for calculation of complex functions in signal processing and wireless communication applications. These functions are the combination of sine and cosine terms that are linked to complex analysis. Pipeline architectures are used in CORDIC algorithm to reduce the critical path, increases the clock speed. An angle recoding method is used to reduce the latency and obtain the desired angle in least number of iteration. In this paper, Hardware efficient Digital sine and cosine wave generator is designed and implemented by using pipelined CORDIC architecture. Digital sine and cosine wave generator is modeled and verified using Xilinx 12.3 ISE. The results shows the significant reduction in critical path, increase in clock speed using pipelining and number of iterations is also reduced by Angle Recoding Method.

The Baugh-Wooley algorithm is a well-known iterative algorithm for performing multiplication in digital signal processing applications. Decomposition logic is used with Baugh-Wooley algorithm to enhance the speed and to reduce the critical path delay. In this paper a high speed multiplier is designed and implemented using decomposition logic and Baugh-Wooley algorithm. The result is compared with booth multiplier. FPGA based architecture is presented and design has been implemented using Xilinx 12.3 device.