D. V. Rama Koti Reddy

Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry

In this paper, several simple and efficient sign based normalized adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancelation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (LMS) based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio and computational complexity.

Adaptive noise removal in the ECG using the Block LMS algorithm

The electrocardiogram (ECG) is the most commonly used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG signals are corrupted by artifacts. So the noise removal is a classical problem in ECG records, that generally produces artifactual data when measuring the ECG parameters. The Block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the mean squared error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. In this paper, we present a BLMS algorithm for removing artifacts preserving the low frequency components and tiny features of the ECG. Finally, we have applied this algorithm on ECG signals from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the BLMS algorithm is superior than the LMS algorithm.

An Efficient Noise Cancellation Technique to Remove Noise from the ECG Signal Using Normalized Signed Regressor LMS Algorithm

In this paper, we present a simple and efficient normalized signed regressor LMS (NSRLMS) algorithm, that can be applied to ECG signal in order to remove various artifacts from them. This algorithm enjoys less computational complexity because of the sign present in the algorithm and good filtering capability because of the normalized term. As a result it is particularly suitable for applications requiring large signal to noise ratios with less computational complexity. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio.

Cancellation of artifacts in ECG Signals using sign based normalized adaptive filtering technique

In this paper, a simple and efficient normalized signed LMS algorithm is proposed for the removal of different kinds of noises from the ECG signal. The proposed implementation is suitable for applications requiring large signal to noise ratios with less computational complexity. The proposed scheme mostly employs simple addition and shift operations and achieves considerable speed up over the other LMS based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio.

Noise cancellation in ECG signals using normalized Sign-Sign LMS algorithm

In this paper, a simple and efficient normalized Sign-Sign LMS algorithm is proposed for the removal of different kinds of noises from the ECG signal. The proposed implementation is suitable for applications requiring large signal to noise ratios with less computational complexity. The proposed scheme mostly employs simple addition and shift operations and achieves considerable speed up over the other LMS based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio.

Baseline wander and Power line interference elimination from Cardiac signals using Error Nonlinearity LMS algorithm

Adaptive filter is a primary method to filter electrocardiogram (ECG) or Cardiac signal, because it does not need the signal statistical characteristics. In this paper we present an adaptive filter for denoising the ECG signal based on Error Nonlinearity Least Mean Square (ENLMS) algorithm. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander and 60 Hz power line interference. Finally, we have applied this algorithm on ECG signals from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the ENLMS based algorithm is superior to that of the LMS based algorithm in noise reduction.

An Efficient Variable Step Size Least Mean Square Adaptive Algorithm Used to Enhance the Quality of Electrocardiogram Signal

The main aim of this paper is to present an efficient method to cancel the noise in the ECG signal, due to various sources, by applying adaptive filtering techniques. The adaptive filter essentially reduces the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. The Least Mean Square (LMS) algorithm is familiar and simple to use for cancellation of noises. However, the low convergence rate and low signal to noise ratio are the limitations for this LMS algorithm. To enhance the performance of LMS algorithm, in this paper, we present an efficient variable step size LMS algorithms which will provide fast convergence at early stages and less misadjustment in later stages. Different kinds of variable step size algorithms are used to eliminate artifacts in ECG by considering the noises such as power line interference and baseline wander. The simulation results shows that the performance of the variable step size LMS algorithm is superior to the conventional LMS algorithm, while for sign based, the sign regressor variable step size LMS algorithm is equally efficient as that of variable step size LMS with additional advantage of less computational complexity.

Effect of Interrupt Logic on Delay Balancing Circuit

Delay elements are added in wave-pipelined circuit to improve the performance of the circuit by reducing the delay difference of the longest and the shortest paths. But it is very difficult to obtain exact delay needed in the circuit. Instead in the present system Interrupt logic is used for delay balancing, thereby providing more feasible and accurate circuit path.

Cancellation of Artifacts in ECG Signals Using Block Adaptive Filtering Techniques

In this chapter, various block-based adaptive filter structures are presented, which estimate the deterministic components of the electrocardiogram (ECG) signal and remove the noise. The familiar Block LMS algorithm (BLMS) and its fast implementation, Fast Block LMS (FBLMS) algorithm, is proposed for removing artifacts preserving the low frequency components and tiny features of the ECG. The proposed implementation is suitable for applications requiring large signal-to-noise ratios with fast convergence rate. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH database and compared its performance with the conventional LMS algorithm. The results show that the performance of the block-based algorithms is superior than the LMS algorithm.

Denoising ECG Signals Using Transform Domain Adaptive Filtering Technique

In this paper, an efficient Fast Block LMS (FBLMS) algorithm is proposed for removing artifacts preserving the low frequency components and tiny features of the ECG. The proposed implementation is suitable for applications requiring large signal to noise ratios with fast convergence rate. The FBLMS algorithm, being the solution of the steepest descent strategy for minimizing the mean squared error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. Finally, we have applied this algorithm on ECG signals from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the FBLMS algorithm is superior than the LMS algorithm. I. INTRODUCTION The extraction of high-resolution ECG signals from record- ings contaminated with background noise is an important issue to investigate. The goal for ECG signal enhancement is to separate the valid signal components from the undesired artifacts, so as to present an ECG that facilitates easy and accurate interpretation. Many approaches have been reported in the literature to address ECG enhancement using adaptive filters (2)-(3), which permit to detect time varying potentials and to track the dynamic variations of the signals. In (3), Thakor et al. proposed an LMS based adaptive recurrent filter to acquire the impulse response of normal QRS complexes, and then applied it for arrhythmia detection in ambulatory ECG recordings. The reference inputs to the LMS algorithm are deterministic functions and are defined by a periodically extended, truncated set of orthonormal basis functions. In these papers, the LMS algorithm operates on an instantaneous basis such that the weight vector is updated every new sample within the occurrence, based on an instantaneous gradient estimate. There are certain clinical applications of ECG signal processing that require adaptive filters with large number of taps. In such applications the conventional LMS algorithm is computationally expensive to implement. The block processing of data samples can significantly reduce the computational complexity. By applying this strategy, a special implementa- tion of the LMS algorithm is called the block LMS algorithm. In a recent study, however, a steady state convergence analysis for the LMS algorithm with deterministic reference inputs showed that the steady-state weight vector is biased, and thus, the adaptive estimate does not approach the Wiener solution. To handle this drawback another strategy was considered for estimating the coefficients of the linear expansion, ie., the BLMS algorithm (8), in which the coefficient vector is updated only once every occurrence based on a block gradient estimation. The BLMS algorithm has been proposed in the case of random reference inputs and has, when the input is stationary, the same steady state misadjustment and convergence speed as the LMS algorithm. A major advantage of the block, or the transform domain, LMS algorithm is that the input signals are approximately uncorrelated. Moreover, the filter output and the weight update terms can be evaluated faster using FFT-based fast BLMS (FBLMS) algorithm. The advantage of this algorithm is less computational complexity and good filtering capability. These characteristics may plays a vital role in biotelemetry, where extraction of noise free ECG signal for efficient diagnosis and fast computations, high data transfer rate are needed to avoid overlapping of pulses and to resolve ambiguities. To the best of our knowledge, transform domain has not been considered previously within the context of filtering artifacts in ECG signals. In this paper, we present a FBLMS algorithm to remove the artifacts from ECG. Such a realization is intrinsically less complex than its BLMS based counterpart. This algorithm enjoys less compu- tational complexity and good filtering capability. To study the performance of the proposed algorithm to effectively remove the noise from the ECG signal, we carried out simulations on MIT-BIH database for different artifacts. The simulation results shows that the proposed algorithm performs better than the LMS counterpart to eliminate the noise from ECG.