Sparse ECG Denoising with Generalized Minimax Concave Penalty

The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated. A novel sparse ECG denoising framework combining low-pass filtering and sparsity recovery is proposed. Two sparsity recovery algorithms are developed based on the traditional ℓ 1 -norm penalty and the novel generalized minimax concave (GMC) penalty, respectively. Compared with the ℓ 1 -norm penalty, the non-differentiable non-convex GMC penalty has the potential to strongly promote sparsity while maintaining the convexity of the cost function. Moreover, the GMC punishes large values less severely than ℓ 1 -norm, which is utilized to overcome the drawback of underestimating the high-amplitude components for the ℓ 1 -norm penalty. The proposed methods are evaluated on ECG signals from the MIT-BIH Arrhythmia database. The results show that underestimating problem is overcome by the proposed GMC-based method. The GMC-based method shows significant improvement with respect to the average of output signal-to-noise ratio improvement ( S N R i m p ), the average of root mean square error (RMSE) and the percent root mean square difference (PRD) over almost any given SNR compared with the classical methods, thus providing promising approaches for ECG denoising.

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Extraction of Voiced Regions of Speech from Emotional Speech Signals Using Wavelet-Pitch Method

Periodica Polytechnica Electrical Engineering and Computer Science ◽

10.3311/ppee.15373 ◽

2021 ◽

Author(s):

Lakshmi Srinivas Dendukuri ◽

Shaik Jakeer Hussain

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Signal To Noise Ratio ◽

Speech Signals ◽

Mean Square ◽

Emotional Speech ◽

Statistical Parameters ◽

Zero Crossing ◽

Previous Frame

Extraction of voiced regions of speech is one of the latest topics in speech domain for various speech applications. Emotional speech signals contain most of the information in voiced regions of speech. In this particular work, voiced regions of speech are extracted from emotional speech signals using wavelet-pitch method. Daubechies wavelet (Db4) is applied on the speech frames after downsampling the speech signals. Autocorrelation function is performed on the extracted approximation coefficients of each speech frame and corresponding pitch values are obtained. A local threshold is defined on obtained pitch values to extract voiced regions. The threshold values are different for male and female speakers, as male pitch values are low compared to the female pitch values in general. The obtained pitch values are scaled down and are compared with the thresholds to extract the voiced frames. The transition frames between the voiced and unvoiced frames are also extracted if the previous frame is voiced frame, to preserve the emotional content in extracted frames. The extracted frames are reshaped to have desired emotional speech signal. Signal to Noise Ratio (SNR), Normalized Root Mean Square Error (NRMSE) and statistical parameters are used as evaluation metrics. This particular work provides better SNR and Normalized Root Mean Square Error values compared to the zero crossing-energy and residual signal based methods in voiced region extraction. Db4 wavelet provides better results compared to Haar and Db2 wavelets in extracting voiced regions using wavelet-pitch method from emotional speech signals.

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Empirical Mode Decomposition (EMD) Based Denoising Method for Heart Sound Signal and Its Performance Analysis

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i5.11344 ◽

2016 ◽

Vol 6 (5) ◽

pp. 2197 ◽

Cited By ~ 1

Author(s):

Amy Hamidah Salman ◽

Nur Ahmadi ◽

Richard Mengko ◽

Armein Z. R. Langi ◽

Tati L. R. Mengko

Keyword(s):

Root Mean Square ◽

Empirical Mode Decomposition ◽

Heart Sound ◽

Signal To Noise Ratio ◽

Sound Signal ◽

Mean Square ◽

Denoising Method ◽

Mode Decomposition ◽

Mean Square Difference ◽

Heart Sound Signal

<p>In this paper, a denoising method for heart sound signal based on empirical mode decomposition (EMD) is proposed. To evaluate the performance of the proposed method, extensive simulations are performed using synthetic normal and abnormal heart sound data corrupted with white, colored, exponential and alpha-stable noise under different SNR input values. The performance is evaluated in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD), and compared with wavelet transform (WT) and total variation (TV) denoising methods. The simulation results show that the proposed method outperforms two other methods in removing three types of noises.</p>

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An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

Applied Sciences ◽

10.3390/app9224968 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4968 ◽

Cited By ~ 4

Author(s):

Dengyong Zhang ◽

Shanshan Wang ◽

Feng Li ◽

Jin Wang ◽

Arun Kumar Sangaiah ◽

...

Keyword(s):

Cardiac Disease ◽

Signal To Noise Ratio ◽

Signal Quality ◽

Ecg Signal ◽

Mean Square ◽

Wavelet Coefficients ◽

Ecg Signals ◽

Smoothing Filter ◽

Wavelet Energy ◽

Mean Square Difference

Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

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Ultrasonic Total Focusing Imaging Method of Multilayer Composite Structures Using the Root-Mean-Square (RMS) Velocity

Advances in Materials Science and Engineering ◽

10.1155/2021/2745732 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xufei Guo ◽

Yan Han

Keyword(s):

Root Mean Square ◽

Composite Structures ◽

Signal To Noise Ratio ◽

Homogeneous Medium ◽

Propagation Path ◽

Multilayer Structures ◽

Imaging Method ◽

Multilayer Composite ◽

Mean Square ◽

Full Matrix

Multilayer composite structures have been widely used in industrial manufacturing, and nondestructive testing of these multilayer structures is to ensure their reliable quality and performance. Currently, ultrasonic total focusing method (TFM) imaging using full-matrix capture (FMC) technology has been proven to sense small defects in a single homogeneous medium and improve the imaging signal-to-noise ratio. However, these algorithms cannot be accurately applied to imaging of multilayer composite structures, due to the acoustic impedance variation and because reflection and refraction occur at the interface between the layers, which makes it very difficult to calculate the ultrasonic propagation path and time. To solve this problem, a root-mean-square (RMS) velocity algorithm for total focusing imaging of multilayer structures is proposed in the article. Based on the theory of RMS velocity for processing of seismic data, the approximated delays can be easily and quickly calculated by a hyperbolic time-distance relationship under circumstances of short lateral distance and horizontal layers. The performance of the proposed algorithm is evaluated by total focusing imaging of a two-layer medium with drilled holes and conducted by the finite element simulation. To further improve imaging efficiency, the partial data in the full-matrix data were used for imaging which is the simplified matrix focusing method (SFM). The results verify that the proposed methods are capable of total focusing imaging of two-layered structures. However, the imaging performance and efficiency of these algorithms are different.

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Signal-To-Noise Ratio Calculations to Validate Sensor Positioning for Facial Muscle Assessment Using Noninvasive Facial Electromyography

Facial Plastic Surgery ◽

10.1055/s-0041-1725168 ◽

2021 ◽

Author(s):

Konstantin Frank ◽

Nicholas Moellhoff ◽

Antonia Kaiser ◽

Michael Alfertshofer ◽

Robert H. Gotkin ◽

...

Keyword(s):

Facial Expression ◽

Root Mean Square ◽

Muscle Activity ◽

Signal To Noise Ratio ◽

Mean Square ◽

Facial Muscle ◽

Signal To Noise ◽

Facial Electromyography ◽

Baseline Noise ◽

Noise Ratio

AbstractThe evaluation of neuromodulator treatment outcomes can be performed by noninvasive surface-derived facial electromyography (fEMG) which can detect cumulative muscle fiber activity deep to the skin. The objective of the present study is to identify the most reliable facial locations where the motor unit action potentials (MUAPs) of various facial muscles can be quantified during fEMG measurements. The study population consisted of five males and seven females (31.0 [12.9] years, body mass index of 22.15 [1.6] kg/m2). Facial muscle activity was assessed in several facial regions in each patient for their respective muscle activity utilizing noninvasive surface-derived fEMG. Variables of interest were the average root mean square of three performed muscle contractions (= signal) (µV), mean root mean square between those contraction with the face in a relaxed facial expression (= baseline noise) (µV), and the signal to noise ratio (SNR). A total of 1,709 processed fEMG signals revealed one specific reliable location in each investigated region based on each muscle's anatomy, on the highest value of the SNR, on the lowest value for the baseline noise, and on the practicability to position the sensor while performing a facial expression. The results of this exploratory study may help guiding future researchers and practitioners in designing study protocols and measuring individual facial MUAP when utilizing fEMG. The locations presented herein were selected based on the measured parameters (SNR, signal, baseline noise) and on the practicability and reproducibility of sensor placement.

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The Optimum Wavelet Base of Wavelet Analysis in Coal Rock Microseismic Signals

Advances in Mechanical Engineering ◽

10.1155/2014/537415 ◽

2014 ◽

Vol 6 ◽

pp. 537415

Author(s):

Shoufeng Tang ◽

Minming Tong ◽

Xinmin He

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Signal To Noise Ratio ◽

Wavelet Denoising ◽

Mean Square ◽

Wavelet Base ◽

Wavelet Bases ◽

Coal Rock ◽

Microseismic Signal

Coal rock rupture microseismic signal is characterized by time-varying, nonstationary, unpredictability, and transient property. Wavelet transform is an important method in microseismic signals processing. However, different wavelet bases yield different results when analyzing the same signal. To study the comparability of different wavelet bases in analyzing microseismic signals, the current paper uses the microseismic signals released from coal rock bursting as the research subject. Through the analysis of the properties of commonly used wavelet basis functions and the characteristics of coal rock microseismic signals, the current study found that Coiflet and Symlet wavelets are suitable for analyzing coal rock microseismic signals. Sym 8 and Coif 2 wavelets were found to be suitable for analyzing and denoising coal rock microseismic signals. After Sym 8 wavelet denoising, signal-to-noise ratio (SNR) and the root mean square error were 30.4184 and 1.3109 E–07, respectively. After Coif 2 wavelet denoising, the SNR and the root mean square error values were 35.2176 and 1.0312 E–07, respectively. The results will aid in the analysis and extraction of coal rock microseismic signals.

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An efficient ECG Denoising method using Discrete Wavelet with Savitzky-Golay filter

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0097 ◽

2019 ◽

Vol 5 (1) ◽

pp. 385-387 ◽

Cited By ~ 2

Author(s):

Fars Samann ◽

Thomas Schanze

Keyword(s):

Signal To Noise Ratio ◽

Gaussian White Noise ◽

Optimal Choice ◽

Discrete Wavelet ◽

Denoising Method ◽

Frame Size ◽

Polynomial Degree ◽

Ecg Signals ◽

Ecg Denoising ◽

Mean Square Difference

AbstractElectrocardiogram (ECG) is a widely used tool for the early diagnosis and evaluation of cardiac disorders. The ECG signal is usually distorted during recording by different types of noise which may lead to incorrect diagnosis. Therefore, clear ECG signals are required to support good cardiac disorder diagnosing. In this paper, an efficient ECG denoising method using combined discrete wavelet with Savitzky-Golay (S-G) filter is proposed. The performance of S-G filter is studied in terms of polynomial degree and frame size, i.e. signal section. In addition, the performance of denoising wavelet is studied in term of mother wavelet type and wavelet order. The advantage of S-G filter is combined with discrete wavelet denoising method to get better denoising performance. The performance of denoising ECG are evaluated using signal to noise ratio (SNR) and percentage root mean square difference (PRD). For this we used simulated and gaussian white noise surrogated ECG signals. Our results show that combined S-G and wavelet filter denoising is noticeable better than the respective individual procedures. In addition, we found that the selection of frame size, order of the S-G filter and the wavelet type and order should be done carefully in order to get optimal results. It also holds true for the new filter that the optimal choice of filter parameters is a compromise between noise reduction and distortion.

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