Anil Mahanta

ECG signal denoising using higher order statistics in Wavelet subbands

Abstract In this work, we propose a novel denoising method based on evaluation of higher-order statistics at different Wavelet bands for an electrocardiogram (ECG) signal. Higher-order statistics at different Wavelet bands provides significant information about the statistical nature of the data in time and frequency. The fourth order cumulant, Kurtosis , and the Energy Contribution Efficiency (ECE) of signal in a Wavelet subband are combined to assess the noise content in the signal. Accordingly, four denoising factors are proposed. Performance of the denoising factors is evaluated and compared with the soft thresholding method. The filtered signal quality is assessed using Percentage Root Mean Square Difference (PRD), Wavelet Weighted Percentage Root Mean Square Difference (WWPRD), and Wavelet Energy-based Diagnostic Distortion (WEDD) measures. It is observed that the proposed denoising scheme not only filters the signal effectively but also helps retain the diagnostic information.

Multichannel ECG Data Compression Based on Multiscale Principal Component Analysis

In this paper, multiscale principal component analysis (MSPCA) is proposed for multichannel electrocardiogram (MECG) data compression. In wavelet domain, principal components analysis (PCA) of multiscale multivariate matrices of multichannel signals helps reduce dimension and remove redundant information present in signals. The selection of principal components (PCs) is based on average fractional energy contribution of eigenvalue in a data matrix. Multichannel compression is implemented using uniform quantizer and entropy coding of PCA coefficients. The compressed signal quality is evaluated quantitatively using percentage root mean square difference (PRD), and wavelet energy-based diagnostic distortion (WEDD) measures. Using dataset from CSE multilead measurement library, multichannel compression ratio of 5.98:1 is found with PRD value 2.09% and the lowest WEDD value of 4.19%. Based on, gold standard subjective quality measure, the lowest mean opinion score error value of 5.56% is found.

Kurtosis-based noise estimation and multiscale energy to denoise ECG signal

In this work, a novel wavelet-based denoising method for electrocardiogram signal is proposed. A threshold is derived by considering energy contribution of a wavelet subband, noise variance which is based on a novel Gaussian measure, Kurtosis, and number of samples. The robust noise estimator, median absolute deviation, is scaled by a normalized wavelet subband Kurtosis instead of conventional statistical quantile function for Gaussian distribution. Signal distortion is evaluated using percentage root mean square difference (PRD), wavelet weighted percentage root mean square difference (WWPRD), and wavelet energy-based diagnostic distortion (WEDD) measures. The results are compared with existing standard thresholding methods. The lowest PRD, WWPRD, and WEDD values are achieved as 9.523, 17.743, and 4.000% for lead-V2, lead-V3, and lead-II signal, respectively. For validation, spatially nonhomogeneous functions like Blocks, Bumps, HeaviSine, and Doppler with noise are evaluated.

Blocking artifacts reduction using adaptive bilateral filtering

In this paper, we propose a simple, non iterative blocking artifacts reduction method for block discrete cosine transform (DCT) compressed images, using adaptive bilateral filter. Bilateral filter when applied on a input image, smooths out the blocking artifacts by weighted averaging of the pixel values without smoothing the edges. The proper selection of the bilateral filter parameters is very important and affects the filtering results significantly. We select the bilateral filter parameters optimally, through empirical study. The bilateral filter parameters are made adaptive to different decompressed images using different quantization tables. Our main contribution in this paper is to select the bilateral filter parameters optimally and adaptively through empirical study, in application to image deblocking. The proposed method shows highly encouraging results both objectively and subjectively, when compared to many state of the art image deblocking schemes including a recent method based on adaptive bilateral filter.

Multiscale wavelet energies and Relative Energy based Denoising of ECG signal

In this work, a novel denoising algorithm based on relative energies of Wavelet subbands and estimated noise variance is proposed for Electrocardiogram (ECG) signal. The proposed algorithm is based on Relative Energy Denoising (RED) factor which is a function of Energy Contribution Efficiency (ECE), Details Energy Contribution Efficiency (DECE) and the estimated noise variance of Wavelet subbands. The algorithm is tested with PTB Diagnostic ECG Database and CSE Multilead Measurement Library. Wavelet filtered signal fidelity is evaluated using Percentage Root Mean Square Difference (PRD), Wavelet Weighted Percentage Root Mean Square Difference (WWPRD) and Wavelet Energy Based Diagnostic Distortion (WEDD) Measure. The results are compared qualitatively and quantitatively with few existing gold standard thresholding methods. The proposed denoising method shows PRD, WWPRD and WEDD values as 5.0966, 14.8554 and 1.0677 respectively.

Multiscale principal component analysis to denoise multichannel ECG signals

In this work, multiscale principal component analysis (MSPCA) is introduced for denoising of multichannel electrocardiogram (MECG) signals. Wavelet decomposition of MECG signals segments the clinical information content at different Wavelet subbands or scales. At subband levels or scales multivariate data matrix are formed using Wavelet coefficients extracted from the same scales of MECG signals. At each subband matrix or scales, PCA is applied for noise elimination. To retain essential diagnostic components, matrices comprising of lower order Wavelet subbands are processed with reduced set of principal component (PC). Qualitative performance is evaluated and quantitative performance of denoising effect is measured by input/output signal-to-noise ratio (SNR). Signal distortion measures are evaluated using percentage root mean square difference (PRD), Wavelet weighted PRD (WWPRD) andWavelet energy based diagnostic distortion measure (WEDD). The proposed algorithm is tested with database of CSE multilead measurement library. The results show significant improvement in denoising of MECG signals with the lowest PRD of 3.488 and high SNR improvement of 34.279 dB.

A New PVT Compensation Technique Based on Current Comparison for Low-Voltage, Near Sub-Threshold LNA

When conventional biasing topologies are employed, near sub-threshold operated amplifiers show large performance deviations under unavoidable PVT variations. Moreover, these effects become severe when these circuits are implemented in sub-nanometer technologies. This paper introduces a new type of compensation technique to realize a reliable low voltage, low-noise amplifier that is achieved by stabilizing the core device trans-conductance (gm). To minimize the gm variation, the proposed technique uses an error voltage generated by comparing the LNA current with a stable constant current reference (CCR). Not only the compensation circuits, a new low-voltage self-biased CCR source is also introduced which is based on conventional β multiplier that can operate with a voltage as low as 0.4 V with a resulting TC (temperature coefficient) of 118 ppm/°C for typical-typical corner case. The gm and S21 of the compensated 65 nm LNA core device shows 8 × times lower variations compared to that of a conventional one when temperature varies from -20 to +110°C and with the consideration of five process corner cases. Finally, Monte Carlo estimation for both process and mismatch shows 34% reduction in standard deviation of S21 and 20% improvement in yield compared to a conventionally biased LNA. The compensated LNA with all its accessories consumes only 402 μW power when operated at a supply voltage of 0.6 V.

Lapped transform-based image denoising with the generalised Gaussian prior

We introduce a new image denoising method based on the statistical modelling of dyadic rearranged lapped transform LT coefficients. Based on Kolomogrov-Smirnov KS goodness of fit test, we have shown that the statistical distribution of the dyadic rearranged LT coefficients in a subband is best approximated by the generalised Gaussian distribution. A Bayesian minimum mean square error MMSE estimator is used to obtain the estimate of noise free coefficients, which is based on modelling the global distribution of the dyadic rearranged LT coefficients using generalised Gaussian distribution. The LT-based image denoising method with generalised Gaussian prior shows highly encouraging both objective and subjective results when compared to several well-known image denoising methods.

Kurtosis based multichannel ECG signal denoising and diagnostic distortion measures

Multichannel Electrocardiogram (MECG) signal de-noising can be described as a process of removing the clinically unimportant contents present from the signal. Higher Order Statistics (HOS) can help to retain finer details of an Electrocardiogram (ECG) signal which can effectively reduce the noise levels in MECG signal. In this work, it is proposed to evaluate the HOS (Kurtosis) in each Wavelet band to denoise an MECG signal. Thresholding levels are derived based on the values of fourth order cumulant, ‘Kurtosis’, of the Wavelet coefficients and Energy Contribution Efficiency (ECE) of Wavelet sub-bands. The performance of this method for compressed signals is evaluated using Percentage Root Mean Square Difference (PRD), Weighted PRD (WPRD), and Wavelet Weighted Percentage Root Mean Square Difference (WWPRD). The proposed algorithm is tested with database of CSE Mutlilead Measurement Library. The results show significant improvement in denoising the MECG signals.

On timing and frequency offset estimation in OFDM systems

We suggest some modifications in the algorithms used for estimation of symbol timing and carrier frequency offset (CFO) in orthogonal frequency division multiplexing (OFDM)-based wireless local area networks (WLANs) using the preamble specified by the 802.11 standardization group. For timing estimate we suggest a modification in the decision variable, which would reduce the uncertainty in the estimate, and for CFO we suggest the use of a repetitive correlation algorithm for higher operating range and at the same time better precision. We present the computer simulations to demonstrate the effectiveness of our suggestions.