Wavelet Energy Based Diagnostic Distortion Research Articles

Alexander fractional differential window filter for ECG denoising.

The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart. During the process of recording and transmission, ECG signals are often corrupted by various types of noises. Minimizations of these noises facilitate accurate detection of various anomalies. In the present paper, Alexander fractional differential window (AFDW) filter is proposed for ECG signal denoising. The designed filter is based on the concept of generalized Alexander polynomial and the R-L differential equation of fractional calculus. This concept is utilized to formulate a window that acts as a forward filter. Thereafter, the backward filter is constructed by reversing the coefficients of the forward filter. The proposed AFDW filter is then obtained by averaging of the forward and backward filter coefficients. The performance of the designed AFDW filter is validated by adding the various type of noise to the original ECG signal obtained from MIT-BIH arrhythmia database. The two non-diagnostic measure, i.e., SNR, MSE, and one diagnostic measure, i.e., wavelet energy based diagnostic distortion (WEDD) have been employed for the quantitative evaluation of the designed filter. Extensive experimentations on all the 48-records of MIT-BIH arrhythmia database resulted in average SNR of 22.014 ± 3.806365, 14.703 ± 3.790275, 13.3183 ± 3.748230; average MSE of 0.001458 ± 0.00028, 0.0078 ± 0.000319, 0.01061 ± 0.000472; and average WEDD value of 0.020169 ± 0.01306, 0.1207 ± 0.061272, 0.1432 ± 0.073588, for ECG signal contaminated by the power line, random, and the white Gaussian noise respectively. A new metric named as morphological power preservation measure (MPPM) is also proposed that account for the power preservance (as indicated by PSD plots) and the QRS morphology. The proposed AFDW filter retained much of the original (clean) signal power without any significant morphological distortion as validated by MPPM measure that were 0.0126, 0.08493, and 0.10336 for the ECG signal corrupted by the different type of noises. The versatility of the proposed AFDW filter is also validated by its application on the ECG signal from MIT-BIH database corrupted by the combination of the noises as well as on the real noisy ECG signals are taken from MIT-BIH ID database. Furthermore, the comparative study has also been done between the proposed AFDW filter and existing state of the art denoising algorithms. The results clearly prove the supremacy of our proposed AFDW filter.

An Integrated Approach Using Chaotic Map & Sample Value Difference Method for Electrocardiogram Steganography and OFDM Based Secured Patient Information Transmission.

This paper presents a patient's confidential data hiding scheme in electrocardiogram (ECG) signal and its subsequent wireless transmission. Patient's confidential data is embedded in ECG (called stego-ECG) using chaotic map and the sample value difference approach. The sample value difference approach effectually hides the patient's confidential data in ECG sample pairs at the predefined locations. The chaotic map generates these predefined locations through the use of selective control parameters. Subsequently, the wireless transmission of the stego-ECG is analyzed using the Orthogonal Frequency Division Multiplexing (OFDM) system in a Rayleigh fading scenario for telemedicine applications. Evaluation of proposed method on all 48 records of MIT-BIH arrhythmia ECG database demonstrates that the embedding does not alter the diagnostic features of cover ECG. The secret data imperceptibility in stego-ECG is evident through the statistical and clinical performance measures. Statistical measures comprise of Percentage Root-mean-square Difference (PRD), Peak Signal to Noise Ratio (PSNR), and Kulback-Leibler Divergence (KL-Div), etc. while clinical metrics includes wavelet Energy Based Diagnostic Distortion (WEDD) and Wavelet based Weighted PRD (WWPRD). The various channel Signal-to-Noise Ratio scenarios are simulated for wireless communication of stego-ECG in OFDM system. The proposed method over all the 48 records of MIT-BIH arrhythmia database resulted in average, PRD=0.26, PSNR=55.49, KL-Div=3.34×10-6, WEDD=0.02, and WWPRD=0.10 with secret data size of 21Kb. Further, a comparative analysis of proposed method and recent existing works was also performed. The results clearly, demonstrated the superiority of proposed method.

ECG compression method based on adaptive quantization of main wavelet packet subbands

In this study, a new compression algorithm for ECG signal is proposed based on selecting important subbands of wavelet packet transform (WPT) and applying subband-dependent quantization algorithm. To this end, first WPT was applied on ECG signal and then more important subbands are selected according to their Shannon entropy. In the next step, content-based quantization and denoising method are applied to the coefficients of the selected subbands. Finally, arithmetic coding is employed to produce compressed data. The performance of the proposed compression method is evaluated using compression rate (CR), percentage root-mean-square difference (PRD) as signal distortion, and wavelet energy-based diagnostic distortion (WEDD) as diagnostic distortion measures on MIT-BIH Arrhythmia database. The average CR of the proposed method is 29.1, its average PRD is <2.9 % and WEDD is <3.2 %. These results demonstrated that the proposed method has a good performance compared to the state-of-the-art compression algorithms.

Exploiting multi-scale signal information in joint compressed sensing recovery of multi-channel ECG signals

Abstract Computational complexity and power consumption are prominent issues in wireless telemonitoring applications involving physiological signals. Compressed sensing (CS) has emerged as a promising framework to address these challenges because of its energy-efficient data reduction procedure. In this work, a CS-based approach is studied for joint compression/reconstruction of multi-channel electrocardiogram (MECG) signals. The MECG signals share spatially correlated cardiac information across the channels, which is exploited in a joint CS framework for improved signal recovery. Weighted mixed-norm minimization (WMNM)-based joint sparse recovery algorithms are proposed, which can successfully recover the signals from all the channels simultaneously by utilizing the joint sparsity of MECG signals in wavelet domain. The proposed algorithms exploit multi-scale signal information through a multi-scale weighting approach. Under this strategy, weights are designed based on the diagnostic information contents of each wavelet subband/scale. In particular, clinically relevant information is captured in the form of subband energy, entropy, and amplitude decay, and based on this weighting rules are defined at each wavelet scale. Such a weighting approach emphasizes nonzero wavelet coefficients having high diagnostic importance during joint CS reconstruction. Insignificant coefficients are deemphasized simultaneously, resulting in a sparser solution. Simulation results using Physikalisch-Technische Bundesanstalt (PTB), Common Standards for Electrocardiography (CSE) and Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) MECG databases show that the proposed methods can achieve superior reconstruction quality with a lower number of measurements compared to their non-weighted counterpart and other existing CS-based methods. Reduction in the required number of measurements reduces computational burden and directly translates into higher compression efficiency, resulting in low energy consumption in CS-based telemonitoring systems. The diagnostic reconstruction quality of the algorithm is validated using diagnostic distortion measures like wavelet energy-based diagnostic distortion (WEDD) and with a post-reconstruction classification task. It achieves a classification accuracy of 73.2% even when MECG signals are jointly reconstructed using only about 10% of compressed measurements, validating the diagnosability of the reconstructed signals even at very low data rates.

Coding ECG beats using multiscale compressed sensing based processing

Compressed sensing recovers a sparse signal from a small set of linear, nonadaptive measurements. A sparse signal can be represented by compressed measurements with a reduced number of projections on a set of random vectors. In this paper, a multiscale compressed sensing based processing is investigated for an electrocardiogram signal which yields coded measurements. In case of an electrocardiogram (ECG) signal, the coded measurements are expected to retain the clinical information. To achieve this, compressed sensing based processing is applied at each wavelet scale and measurements are coded using Huffman coder. The measurements at each scale use random sensing matrix with independent identically distributed (i.i.d.) entries formed by sampling a Gaussian distribution. The proposed method is evaluated using pathological ECG signals from the CSE database, synthetic and normal ECGs. It helps preserve the pathological information and clinical components in compressed signal. The compressed signal quality is evaluated using standard distortion measures and mean opinion score (MOS). The MOS values for the signals range from 5% to 8.3% with a wavelet energy based diagnostic distortion (WEDD) value of 9.46% which falls under the excellent category.

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.

A Hybrid Algorithm for Classification of Compressed ECG

Efficient compression reduces memory requirement in long term recording and reduces power and time requirement in transmission. A new compression algorithm combining Linear Predictive coding (LPC) and Discrete Wavelet transform is proposed in this study. Our coding algorithm offers compression ratio above 85% for records of MIT-BIH compression database. The performance of algorithm is quantified by computing distortion measures like percentage root mean square difference (PRD), wavelet-based weighted PRD (WWPRD) and Wavelet energy based diagnostic distortion (WEDD). The PRD is found to be below 6 %, values of WWPRD and WEDD are less than 0.03. Classification of decompressed signals, by employing fuzzy c means method, is achieved with accuracy of 97%.

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.

ECG signal denoising using higher order statistics in Wavelet subbands

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.

Wavelet energy based diagnostic distortion measure for ECG

In this paper, a novel Wavelet Energy based diagnostic distortion (WEDD) measure is proposed to assess the reconstructed signal quality for ECG compression algorithms. WEDD is evaluated from the Wavelet coefficients of the original and the reconstructed ECG signals. For each ECG segment, a Wavelet energy weight vector is computed via five-level biorthogonal discrete Wavelet transform (DWT). WEDD is defined as the sum of Wavelet energy weighted percentage root mean square difference of each subband. The effectiveness of this measure is validated by linear (linear polynomial and cubic polynomial) and nonlinear (logistic) regression analysis between the computed WEDD values and the mean opinion score (MOS) given by cardiologists. WEDD provides a better prediction accuracy and exhibits a statistically better monotonic relationship with the MOS ratings than Wavelet based weighted percentage root mean square difference (PRD) measure (WWPRD), PRD and other objective measures. Standard correlation coefficient and Spearman rank-order correlation coefficient (SROCC) between the WEDD/MOS ratings is 0.969 and 0.9624, respectively.