Rafi Ahamed Shaik

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.

Efficient and Simplified Adaptive Noise Cancelers for ECG Sensor Based Remote Health Monitoring

In this paper, several simple and efficient sign and error nonlinearity-based adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancellation 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.

Low-Area and High-Throughput Architecture for an Adaptive Filter Using Distributed Arithmetic

A high-performance implementation scheme for a least mean square adaptive filter is presented. The architecture is based on distributed arithmetic in which the partial products of filter coefficients are precomputed and stored in lookup tables (LUTs) and the filtering is done by shift-and-accumulate operations on these partial products. In the case of an adaptive filter, it is required that the filter coefficients be updated and, hence, these LUTs are to be recalculated. A new strategy based on the offset binary coding scheme has been proposed in order to update these LUTs from time to time. Simulation results show that the proposed scheme consumes very less chip area and operates at high throughput for large base unit size k ( = N/m) , where m is an integer and N is the number of filter coefficients. For example, a 128-tap finite-impulse-response adaptive filter with the proposed implementation produces 12 times more throughput (for k = 8) and consumes almost 26% less area when compared to the best of existing architectures.

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.

FPGA implementation of Fast Block LMS adaptive filter using Distributed Arithmetic for high throughput

This paper proposes a design and implementation of high throughput adaptive digital filter using Fast Block Least Mean Squares (FBLMS) adaptive algorithm. The filter structure is based on Distributed Arithmetic (DA), which is able to calculate the inner product by shifting, and accumulating of partial products and storing in look-up table, also the desired adaptive digital filter will be multiplierless. Thus a DA based implementation of adaptive filter is highly computational and area efficient. Furthermore, the fundamental building blocks in the DA architecture map well to the architecture of todays Field Programmable Gate Arrays (FPGA). FPGA implementation results conforms that the proposed DA based adaptive filter can implement with significantly smaller area usage, (about 45%) less than that of the existing FBLMS algorithm based adaptive filter.

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.

Removal of EOG Artifacts From Single Channel EEG Signals Using Combined Singular Spectrum Analysis and Adaptive Noise Canceler

The electroencephalogram (EEG) signals represent the electrical activity of the brain. In applications, such as brain-computer interface (BCI), features of the EEG signals are used to control the devices. However, while recording, EEG signals often contaminated by electrooculogram (EOG) artifacts; such artifacts degrade the performance of the BCI. In this paper, we proposed a new technique using singular spectrum analysis (SSA) and adaptive noise canceler (ANC) to remove the EOG artifact from the contaminated EEG signal. In this technique, first, we proposed a novel grouping technique for SSA to construct the reference signal (EOG) for ANC. Later, using the extracted reference signal, the adaptive filter was employed to remove EOG artifact from the contaminated EEG signal. To quantify the performance of the proposed technique, we carried out simulations on synthetic and real-life EEG signals. In terms of relative root mean square error and mean absolute error, the proposed SSA-ANC method outperforms the existing techniques.

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.

A Block-Floating-Point-Based Realization of the Block LMS Algorithm

An efficient scheme is proposed for implementing the block LMS (BLMS) algorithm in a block-floating-point framework that permits processing of data over a wide dynamic range at a processor complexity and cost as low as that of a fixed-point processor. The proposed scheme adopts appropriate formats for representing the filter coefficients and the data. Using these and a new upper bound on the step size, update relations for the filter weight mantissas and exponent are developed, taking care so that neither overflow occurs, nor are quantities which are already very small multiplied directly. It is further shown how the mantissas of the filter coefficients and also the filter output can be evaluated faster by suitably modifying the approach of the fast BLMS algorithm

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.