Improvement of signal to noise ratio (SNR) in ECG signals based on dual-band continuous wavelet transform

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

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Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Entropy ◽

10.3390/e23010119 ◽

2021 ◽

Vol 23 (1) ◽

pp. 119

Author(s):

Tao Wang ◽

Changhua Lu ◽

Yining Sun ◽

Mei Yang ◽

Chun Liu ◽

...

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Convolutional Neural Network ◽

Continuous Wavelet Transform ◽

Continuous Wavelet ◽

Time Frequency ◽

Ecg Signals ◽

Rr Interval ◽

Frequency Components ◽

Fully Connected

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e9632.069520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 541-546

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Mean Square Error ◽

Signal To Noise Ratio ◽

Image Steganography ◽

Discrete Wavelet ◽

Mean Square ◽

Signal To Noise ◽

Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

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Signal to Noise Ratio Enhancement Using Empirical Wavelet Transform

10.2523/iptc-19569-ms ◽

2019 ◽

Author(s):

Wei Yi Lee ◽

Rosita Hamidi ◽

Deva Ghosh ◽

Mohd Hafiz Musa

Keyword(s):

Wavelet Transform ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Empirical Wavelet Transform ◽

Noise Ratio

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Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals

Fluctuation and Noise Letters ◽

10.1142/s0219477520500273 ◽

2020 ◽

Vol 19 (03) ◽

pp. 2050027

Author(s):

Thandar Oo ◽

Pornchai Phukpattaranont

Keyword(s):

Signal To Noise Ratio ◽

Training Data ◽

Signal To Noise ◽

Zero Crossing ◽

Ecg Signals ◽

Average Value ◽

Emg Signal ◽

Recognition Systems ◽

Noise Ratio ◽

Electromyography Signals

When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.

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Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

International Journal of Biomedical Imaging ◽

10.1155/2017/9604178 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13

Author(s):

Shanshan Chen ◽

Bensheng Qiu ◽

Feng Zhao ◽

Chao Li ◽

Hongwei Du

Keyword(s):

Wavelet Transform ◽

Compressed Sensing ◽

Discrete Wavelet Transform ◽

Signal To Noise Ratio ◽

Similarity Index ◽

Discrete Wavelet ◽

Translation Invariance ◽

Signal To Noise ◽

Reconstruction Methods ◽

Noise Ratio

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.

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Circulant Matrix-Based Continuous Wavelet Transform for Achieving Low Complexity Electrocardiogram Feature Extraction in Health Monitoring Applications

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8659 ◽

2020 ◽

Vol 17 (1) ◽

pp. 254-259

Author(s):

Harikrishna Ponnam ◽

Jakeer Hussain Shaik

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Computational Complexity ◽

Error Rate ◽

Continuous Wavelet Transform ◽

Low Complexity ◽

Circulant Matrix ◽

Continuous Wavelet ◽

Ecg Signals ◽

Gradient Based

In the application of remote cardiovascular monitoring, the computational complexity and power consumption need to be maintained in a considerable level in order to prevent the limitations introduced by the computationally constrained equipment’s that perform the process of continuous monitoring and analysis. In this paper, a Circulant Matrix-based Continuous Wavelet Transform (CM-CWT)-based feature extraction mechanism is contributed to minimizing the computational complexity incurred during the process of feature extraction from the input ECG signals. This proposed CM-CWT mechanism derives the advantages of the Circulant Matrix-based Continuous Wavelet Transform and Gradient-based filtering design for achieving excellent feature extraction from ECG signals with low computational complexity. The experimental investigation of the proposed CM-CWT mechanism is conducted using the factors of computational complexity, sensitivity, prediction accuracy and error rate for estimating its predominance over the compared DWT-HAAR and HIFEA approaches used for ECG feature extraction. The experiments of the proposed CM-CWT mechanism on an average is estimated to reduce the error rate to the maximum of 21% compared to the existing DWT-HAAR and HIFEA approaches used for ECG feature extraction.

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Frequency Domain Design Method of Wavelet Basis Based on Pulsar Signal

Journal of Navigation ◽

10.1017/s0373463320000211 ◽

2020 ◽

Vol 73 (6) ◽

pp. 1223-1236

Author(s):

Sihai You ◽

Hongli Wang ◽

Yiyang He ◽

Qiang Xu ◽

Lei Feng

Keyword(s):

Wavelet Transform ◽

Frequency Domain ◽

Design Method ◽

Signal To Noise Ratio ◽

Frequency Domain Analysis ◽

Time Of Arrival ◽

Frequency Noise ◽

Wavelet Basis ◽

Signal To Noise ◽

Noise Ratio

During pulsar navigation, the high-frequency noise carried by the pulsar profile signal reduces the accuracy of the pulse TOA (Time of Arrival) estimation. At present, the main method to remove signal noise by using wavelet transform is to redesign the function of the threshold and level of wavelet transform. However, the signal-to-noise ratio and other indicators of the filtered signal need to be further optimised, so a more appropriate wavelet basis needs to be designed. This paper proposes a wavelet basis design method based on frequency domain analysis to improve the denoising effect of pulsar signals. This method first analyses the pulsar contour signal in the frequency domain and then designs a Crab pulsar wavelet basis (CPn, where n represents the wavelet basis length) based on its frequency domain characteristics. In order to improve the real-time performance of the algorithm, a wavelet lifting scheme is implemented. Through simulation, this method analyses the pulsar contour signal data at home and abroad. Results show the signal-to-noise ratio can be increased by 4 dB, the mean square error is reduced by 61% and the peak error is reduced by 45%. Therefore, this method has better filtering effect.

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