Biblio

ECG signals are often corrupted by 50 Hz noise, the frequency from the power supply. So it becomes quite necessary to remove Power Line Interference (PLI) from the ECG signal. The reference ECG signal data was taken from the MIT-BIH database. Different filtering techniques comprising of Discrete Wavelet Transform (DWT), Normalized Least Mean Square (NLMS) filter, Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter were used in this paper for denoising the ECG signal which was corrupted by the PLI. Later, the comparison was made among the methods, to find the best methodology to denoise the corrupted ECG signal. The parameters that were used for the comparison are Mean Square Error (MSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR). Higher values of SNR & PSNR and lower values of MSE & MAE define the best denoising algorithm.

This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the γ-filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and electroencephalographic time series prediction, complex nonlinear system identification, and adaptive antenna array processing demonstrate the potential of the approach for scenarios where recursivity and nonlinearity have to be readily combined.