SNR Improvement Research Articles

Hybrid aperiodic coding for SNR improvement in a BOTDA fiber sensor.

The measurement accuracy of a Brillouin optical time domain analysis (BOTDA) fiber sensor is determined by the signal-to-noise ratio (SNR) of the received sensing signal. Here, a new hybrid aperiodic coding method is proposed to improve the SNR. In the proposed method, two pre-discovered short seed aperiodic codes (SA-codes) are used to construct a new hybrid aperiodic code (HA-code) in a nested way. The HA-code inherits the good denoising capabilities of the two SA-codes and features a high coding gain. In the proof-of-concept experiment, a SNR improvement up to 8 dB is obtained, which improves the measurement certainty to 1.67 MHz over a 117.46 km sensing range under a spatial resolution of 2.6 m.

Enhancing the Performance Characteristics of Piezoelectric Transducers by Broadband Tuning

Effect of broadband tuning in improving the performance characteristics, especially in receive mode, of a piezoelectric transducer working in the sonic frequencies is presented. The broadband tuning technique results in a frequency response function characteristic of a bandpass filter with significant improvements in SNR. When represented by an electrical equivalent circuit, the transducer resembles a half-section bandpass filter. Filter of higher order is obtained by adding external components for enhancing the bandwidth. The circuit parameters are determined from the measured electrical admittance of the transducer in the water. The transducer characteristics with broadband tuning network are simulated and validated experimentally. The impedance phase has improved from −70° to 0 ±20° over a broadband leading to an increase in power factor from 0.3 to close to 1. An enhancement of 3 dB in Source Level, 10 dB in Receiving Sensitivity, and 10 dB in Figure of Merit are obtained with 11 kHz increase in bandwidth. An improvement of 7 dB in the noise figure and SNR is also obtained in the receiving mode. The model results are in close agreement with the measured data validating the model. It is found that the broadband tuning network enhances the transducer performance simultaneously in transmitting and receiving modes. This is a significant improvement over conventional shunt tuning and is useful for stand-alone, energy-efficient sonars and two-way acoustic communication systems. The present studies demonstrate that broadband tuning methods in general, originally developed for RF and ultrasonic domains, are applicable in the sonic frequencies also.

Deep learning enables confocal laser-scanning microscopy with enhanced resolution.

Theoretical resolution enhancement of confocal laser-scanning microscopy (CLSM) is sacrificed for the best compromise between optical sectioning and the signal-to-noise ratio (SNR). The pixel reassignment reconstruction algorithm can improve the effective spatial resolution of CLSM to its theoretical limit. However, current implementations are not versatile and are time-consuming or technically complex. Here we present a parameter-free post-processing strategy for laser-scanning microscopy based on deep learning, which enables a spatial resolution enhancement by a factor of ∼1.3, compared to conventional CLSM. To speed up the training process for experimental data, transfer learning, combined with a hybrid dataset consisting of simulated synthetic and experimental images, is employed. The overall resolution and SNR improvement, validated by quantitative evaluation metrics, allowed us to correctly infer the fine structures of real experimental images.

Pixel-reassignment in ultrasound imaging

We present an adaptation of the pixel-reassignment technique from confocal fluorescent microscopy to coherent ultrasound imaging. The method, ultrasound pixel-reassignment (UPR), provides a resolution and signal to noise (SNR) improvement in ultrasound imaging by computationally reassigning off-focus signals acquired using traditional plane wave compounding ultrasonography. We theoretically analyze the analogy between the optical and ultrasound implementations of pixel reassignment and experimentally evaluate the imaging quality on tissue-mimicking acoustic phantoms. We demonstrate that UPR provides a 25% resolution improvement and a 3 dB SNR improvement in in vitro scans without any change in hardware or acquisition schemes.

Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection

A non-local patch regression (NLPR) denoising-enhanced differential broadband photoacoustic (PA) sensor was developed for the high-sensitive detection of multiple trace gases. Using the edge preservation index (EPI) and signal-to-noise ratio (SNR) as a dual-criterion, the fluctuation was dramatically suppressed while the spectral absorption peaks were maintained by the introduction of a NLPR algorithm. The feasibility of the broadband framework was verified by measuring the C2H2 in the background of ambient air. A normalized noise equivalent absorption (NNEA) coefficient of 6.13 × 10−11 cm−1·W·Hz−1/2 was obtained with a 30-mW globar source and a SNR improvement factor of 23. Furthermore, the simultaneous multiple-trace-gas detection capability was determined by measuring C2H2, H2O, and CO2. Following the guidance of single-component processing, the NLPR processed results showed higher EPI and SNR compared to the spectra denoised by the wavelet method and the non-local means algorithm. The experimentally determined SNRs of the C2H2, H2O, and CO2 spectra were improved by a factor of 20. The NNEA coefficient reached a value of 7.02 × 10−11 cm−1·W·Hz−1/2 for C2H2. The NLPR algorithm presented good performance in noise suppression and absorption peak fidelity, which offered a higher dynamic range and was demonstrated to be an effective approach for trace gas analysis.

Study of optical reflectors for a 100ps coincidence time resolution TOF-PET detector design

Positron Emission Tomography (PET) reconstructed image signal-to-noise ratio (SNR) can be improved by including the 511 keV photon pair coincidence time-of-flight (TOF) information. The degree of SNR improvement from this TOF capability depends on the coincidence time resolution (CTR) of the PET system, which is essentially the variation in photon arrival time differences over all coincident photon pairs detected for a point positron source placed at the system center. The CTR is determined by several factors including the intrinsic properties of the scintillation crystals and photodetectors, crystal-to-photodetector coupling configurations, reflective materials, and the electronic readout configuration scheme. The goal of the present work is to build a novel TOF-PET system with 100 picoseconds (ps) CTR, which provides an additional factor of 1.5–2.0 improvement in reconstructed image SNR compared to state-of-the-art TOF-PET systems which achieve 225–400 ps CTR. A critical parameter to understand is the optical reflector’s influence on scintillation light collection and transit time variations to the photodetector. To study the effects of the reflector covering the scintillation crystal element on CTR, we have tested the performance of four different reflector materials: Enhanced Specular Reflector (ESR) –coupled with air or optical grease to the scintillator; Teflon tape; BaSO4 paint alone or mixed with epoxy; and TiO2 paint. For the experimental set-up, we made use of 3 × 3 × 10 mm3 fast-LGSO:Ce scintillation crystal elements coupled to an array of silicon photomultipliers (SiPMs) using a novel ‘side-readout’ configuration that has proven to have lower variations in scintillation light collection efficiency and transit time to the photodetector. Results: show CTR values of 102.0 ± 0.8, 100.2 ± 1.2, 97.3 ± 1.8 and 95.0 ± 1.0 ps full-width-half-maximum (FWHM) with non-calibrated energy resolutions of 10.2 ± 1.8, 9.9 ± 1.2, 7.9 ± 1.2, and 8.6 ± 1.7% FWHM for the Teflon, ESR (without grease), BaSO4 (without epoxy) and TiO2 paint treatments, respectively.

External Dynamic InTerference Estimation and Removal (EDITER) for low field MRI.

Point-of-care MRI requires operation outside of Faraday shielded rooms normally used to block image-degrading electromagnetic interference (EMI). To address this, we introduce the EDITER method (External Dynamic InTerference Estimation and Removal), an external sensor-based method to retrospectively remove image artifacts from time-varying external interference sources. The method acquires data from multiple EMI detectors (tuned receive coils as well as untuned electrodes placed on the body) simultaneously with the primary MR coil during and between image data acquisition. We calculate impulse response functions dynamically that map the data from the detectors to the time varying artifacts then remove the transformed detected EMI from the MR data. Performance of the EDITER algorithm was assessed in phantom and in vivo imaging experiments in an 80mT portable brain MRI in a controlled EMI environment and with an open 47.5mT MRI scanner in an uncontrolled EMI setting. In the controlled setting, the effectiveness of the EDITER technique was demonstrated for specific types of introduced EMI sources with up to a 97% reduction of structured EMI and up to 76% reduction of broadband EMI in phantom experiments. In the uncontrolled EMI experiments, we demonstrate EMI reductions of up to 99% using an electrode and pick-up coil in vivo. We demonstrate up to a nine-fold improvement in image SNR with the method. The EDITER technique is a flexible and robust method to improve image quality in portable MRI systems with minimal passive shielding and could reduce the reliance of MRI on shielded rooms and allow for truly portable MRI.

Quadratic phase coding for SNR improvement in time-expanded phase-sensitive OTDR.

Time-expanded phase-sensitive optical time-domain reflectometry (TE-ΦOTDR) is a dual-comb-based distributed optical fiber sensing technique capable of providing centimeter scale resolution while maintaining a remarkably low (MHz) detection bandwidth. Random spectral phase coding of the dual combs involved in the fiber interrogation process has been proposed as a means of increasing the signal-to-noise ratio (SNR) of the sensor. In this Letter, we present a specific spectral phase coding methodology capable of further enlarging the SNR of TE-ΦOTDR. This approach is based on the use of a quadratic spectral phase to precisely control the peak power of the comb signals. As a result, an SNR improvement of up to 8 dB has been experimentally attained with respect to that based on the random phase coding previously reported.

SNR improvement based on piecewise linear interpolation

Abstract Interpolation improves the resolution of the curve. Based on the stationary characteristics of the signal and the non-stationary characteristics of the noise, the theoretical proof indicates that the piecewise linear interpolation can improve the signal-to-noise ratio, which is further confirmed by simulation results.

Adaptive Real-Time Wavelet Denoising Architecture Based on Feedback Control Loop

The discrete wavelet transform is commonly used as a denoising step for many applications, like biomedical applications which are usually suffering from low SNR of the recorded signal. However, the choice of appropriate threshold value for DWT coefficients plays significant role in reconstructing the denoised signal. This paper presents a design of real-time wavelet denoising architecture which is suitable for wide range of real-time denoising applications. In this design, an adaptive thresholding approach based on feedback control loop is proposed to make the architecture more applicable for real-time wavelet denoising. This thresholding method considers a noise level estimator module based on first detail coefficients level 𝑑1 to calculate the unknown standard deviation of background noise. The proposed architecture is developed using MATLAB to simulate the suggested denoising method. The performance of the proposed denoising method is studied in terms of integral gain 𝐺 of feedback control and window size 𝑀 with respect to the improvement in SNR and settling time. The results imply that the proposed denoising architecture is suitable for real-time denoising applications with acceptable improvement in SNR approximately 8 dB.

In vivo derivative NMR spectroscopy for simultaneous improvements of resolution and signal-to-noise-ratio: Case study, Glioma

The theme of this study is derivative nuclear magnetic resonance (dNMR) spectroscopy. This versatile methodology of peering into the molecular structure of general matter is common to e.g. analytical chemistry and medical diagnostics. Theoretically, the potential of dNMR is huge and the art is putting it into practice. The implementation of dNMR (be it in vitro or in vivo) is wholly dependent on the manner in which the encoded time signals are analyzed. These acquired data contain the entire information which is, however, opaque in the original time domain. Their frequency-dependent dual representation, a spectrum, can be transparent, provided that the appropriate signal processors are used. In signal processing, there are shape and parameter estimators. The former processors are qualitative as they predict only the forms of the lineshape profiles of spectra. The latter processors are quantitative because they can give the peak parameters (positions, widths, heights, phases). Both estimators can produce total shape spectra or envelopes. Additionally, parameter estimators can yield the component spectra, based on the reconstructed peak quantifiers. In principle, only parameter estimators can solve the quantification problem (harmonic inversion) to determine the structure of the time signal and, hence, the quantitative content of the investigated matter. The derivative fast Fourier transform (dFFT) and the derivative fast Padé transform (dFPT) are the two obvious candidates to employ for dNMR spectroscopy. To make fair comparisons between the dFFT and dFPT, the latter should also be applied as a shape estimator. This is what is done in the present study, using the time signals encoded from a patient with brain tumor (glioma) using a 1.5T clinical scanner. Moreover, within the dFPT itself, the shape estimations are compared to the parameter estimations. The goal of these testings is to see whether, for in vivo dNMR spectroscopy, shape estimations by the dFPT could quantify (without fitting), similarly to parameter estimations. We check this key point in two successive steps. First, we compare the envelopes from the shape and parameter estimations in the dFPT. The second comparison is between the envelopes and components from the shape and parameter estimations, respectively, in the dFPT. This plan for benchmarking shape estimations by the dFPT is challenging both on the level of data acquisition and data analysis. The data acquisition reported here provides encoded time signals of short length, only 512 as compared to 2048, which is customarily employed. Moreover, the encoding echo time was long (272 ms) at which most of resonances assigned to metabolites with shorter spin-spin relaxations are likely to be obliterated from the frequency spectra. Yet, in face of such seemingly insurmountable obstacles, we are looking into the possibility to extract diagnostically relevant information, having particularly in focus the resonances for recognized cancer biomarkers, notably lactate, choline and phosphocholine. Further, we want to see how many of the remaining resonances in the spectra could accurately be identified with clinical reliability as some of them could also be diagnostically relevant. From the mathematical stance, we are here shaking the sharp border between shape and parameter estimators. That border stood around for a long time within nonderivative estimations. However, derivative shape estimations have a chance to tear the border down. Recently, shape estimations by the dFPT have been shown to lead such a trend as this processor could quantify using the time signals encoded from a phantom (a test sample of known content). Further, the present task encounters a number of additional challenges, including a low signal-to-noise ratio (SNR) and, of course, the unknown content of the scanned tissue. Nevertheless, we are determined to find out whether the nonparametric dFPT can deliver the unique quantification-equipped shape estimation and, thus, live up to the expectation of derivative processing: a long-sought simultaneous improvement of resolution and SNR. In every facet of in vivo dNMR, we found that shape estimations by the dFPT has successfully passed the outlined most stringent tests. It begins with transforming itself to a parameter estimator (already with the 3rd and 4th derivatives). It ends with reconstructing some 54 well-isolated resonances. These include the peaks assigned to recognized cancer biomarkers. In particular, a clear separation of choline from phosphocholine is evidenced for the first time by reliance upon the dFPT with its shape estimations alone.

Adaptive Cylindrical Wireless Metasurfaces in Clinical Magnetic Resonance Imaging.

The signal-to-noise ratio (SNR) is one of the most important criteria for evaluating the image quality in magnetic resonance imaging (MRI), and metasurfaces with unique electromagnetic properties provide a novel method for SNR improvement. However, their applications in clinical MRI are highly restricted by the inhomogeneous enhancement of the magnetic field and interference in the radio frequency (RF) transmitting field. In this study, an adaptive cylindrical wireless metasurface (ACWM) with homogeneous field enhancement and adaptive resonant modes is reported. The ACWM automatically switches its resonant modes between the partial (transmitting period) and whole (receiving period) resonance, which enables it to not only eliminate the interference in RF transmitting field, but also greatly enhance the SNR. Its adaptability also makes the ACWM applicable to all common clinical sequences without any modifications in the scan parameters. The SNR of MRI images of the human wrist, acquired with ACWM, is two to four times compared with the conventional coil. This work offers a practical control method to fill the scientific knowledge gaps between the preclinical research and medical applications for metasurfaces, and suggests a novel and powerful tool for diagnosing and evaluating human diseases.

Au Hierarchical Nanostructure-Based Surface Modification of Microelectrodes for Improved Neural Signal Recording.

Microelectrodes are widely used for neural signal analysis because they can record high-resolution signals. In general, the smaller the size of the microelectrode for obtaining a high-resolution signal, the higher the impedance and noise value of the electrodes. Therefore, to improve the signal-to-noise ratio (SNR) of neural signals, it is important to develop microelectrodes with low impedance and noise. In this research, an Au hierarchical nanostructure (AHN) was deposited to improve the electrochemical surface area (ECSA) of a microelectrode. Au nanostructures on different scales were deposited on the electrode surface in a hierarchical structure using an electrochemical deposition method. The AHN-modified microelectrode exhibited an average of 80% improvement in impedance compared to a bare microelectrode. Through electrochemical impedance spectroscopy analysis and impedance equivalent circuit modeling, the increase in the ECSA due to the AHN was confirmed. After evaluating the cell cytotoxicity of the AHN-modified microelectrode through an in vitro test, neural signals from rats were obtained in in vivo experiments. The AHN-modified microelectrode exhibited an approximate 9.79 dB improvement in SNR compared to the bare microelectrode. This surface modification technology is a post-treatment strategy used for existing fabricated electrodes, so it can be applied to microelectrode arrays and nerve electrodes made from various structures and materials.

A new stationary grid, with grid lines aligned to pixel lines with submicron‐order precision, to suppress grid artifacts

We have developed a new stationary grid named a pixel-aligned grid (PA grid), in which the grid lines are aligned to the pixel lines with submicron-order precision. Further, we have evaluated its performance relative to that of a conventional grid combined with grid-line removal (GLR) processing. A flat-panel detector system of an indirect type, with a pixel pitch of 150μm, was employed. Four PA grids having a grid ratio of 6:1 associated with abdominal bedside radiography, with the grid-line pitch (GP) varied around the target value of 150μm, were produced. Blank images were obtained with four PA grids for measuring the period and amplitude of the grid artifact. In performance evaluation, acrylic and anthropomorphic abdominal phantom images were used with the PA grid, a conventional grid (40 lines/cm, grid ratio 6:1), and no grids. The grid artifacts were evaluated by power spectrum (PS) analysis. Also, the signal-to-noise ratio (SNR) improvement factor (KSNR ) was measured. Grid artifacts were hardly recognizable with PA grids with GP errors of 0.3μm and 0.6μm because of the prolonged grid artifact periods. The measured artifact amplitudes of these PA grids were less than 0.6%. Furthermore, the PA grids did not produce notable frequency peaks in PS. In contrast, the conventional grid without GLR processing produced two conspicuous peaks. With GLR processing, notable reductions in PS were observed around the two peak frequencies, which caused blurring in bone structures. For the acrylic thickness of 20cm, the KSNR s for the PA grid were around 1.4, suggesting some SNR improvement in abdominal bedside radiography. The present study has demonstrated that PA grids with their grid-line pitches close to the pixel-line pitch within errors of 0.6μm produce grid artifact-free images without any signal losses. Thus, the proposed PA grid will prove to be effective and useful in various clinical applications.

Capacity and BER performance improvement in integrated MIMO‐OFDM system using optimal power allocation, channel estimation, and turbo coding

SummaryAmong communication systems, multiple input multiple output (MIMO) system has the capability of providing spectral efficiency, high data rates, diversity gain, and improved reliability. Suitably designed orthogonal frequency division multiplexing (OFDM) transmits the signal without inter symbol interference and multipath fading. Hence, MIMO‐OFDM is a strong candidate for 4G and 5G wireless systems. However, variations in signal‐to‐noise ratio (SNR) due to the random nature of the channel and poor capacity at low SNR are critical issues in conventional MIMO‐OFDM system. This paper explains how optimal power allocation (OPA), channel estimation, and coding improve the capacity and bit error rate (BER) performance of MIMO‐OFDM system. In OPA, the transmitter power is distributed adaptively to the channels based on the SNR statistics using singular value decomposition and water filling algorithm. In case of low SNR, less number of channels are selected for power allocation. Least squares (LS), minimum mean square error (MMSE), and least mean square (LMS) estimators are considered in comb type pilot channel estimation while turbo code is used with code rates of 1/2 and 1/3. From the simulation results, capacity improvement of 5 b/s/Hz and 11 b/s/Hz is observed for MIMO‐OFDM systems with four antennas and eight antennas, respectively. It is also found that performance of LMS estimator improved by 3.5‐dB SNR over LS and 1 dB SNR over MMSE. In this paper is shown how LMS estimator and 1/3 turbo code have been integrated with MIMO‐OFDM, and this has resulted in around 4‐dB SNR improvement over conventional MIMO‐OFDM.

Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches.

Touchscreens have been studied and developed for a long time to provide user-friendly and intuitive interfaces on displays. This paper describes the touchscreen technologies in four categories of resistive, capacitive, acoustic wave, and optical methods. Then, it addresses the main studies of SNR improvement and stylus support on the capacitive touchscreens that have been widely adopted in most consumer electronics such as smartphones, tablet PCs, and notebook PCs. In addition, the machine learning approaches for capacitive touchscreens are explained in four applications of user identification/authentication, gesture detection, accuracy improvement, and input discrimination.

Circular shaped microelectrodes for single cell electrical measurements for lab-on-a-chip applications.

The impedimetric sensing techniques for single cell characterization have witnessed growing interest due to their high sensitivity and widespread applications. However, adapting the method to different biological measurements in microfluidic environments under various input conditions can result in feeble signal detection leading to a drastic decrease in the sensor sensitivity. The reduced signal-to-noise ratio (SNR) hinders the signal differentiation, sensor accuracy and prohibits fully integrated point-of-care applications. Here, we address the sensitivity enhancement for microfluidic impedimetric sensing of micron and submicron-sized microparticles by exploring novel circular shape electrodes in a simulation study. The influence of radial electrode parameters on differential electrical signal is systematically analyzed in COMSOL Multiphysics using spherical particles ranging from 0.75µm to 5µm in diameter. Detailed analysis revealed the strong impact of the circular shape microelectrode geometry and the electrode gap on the signal strength, resulting SNR, and device sensitivity for multiple bioparticles detection. Specifically, ˃ 50dB improvement in SNR was enabled by optimizing the circular electrode geometrical parameters. Our proposed sensing modality can be adaptedfor nanoparticles detection by further optimizing the microfluidic device parameters.

Components loss for neural networks in mask-based speech enhancement

Estimating time-frequency domain masks for single-channel speech enhancement using deep learning methods has recently become a popular research field with promising results. In this paper, we propose a novel components loss (CL) for the training of neural networks for mask-based speech enhancement. During the training process, the proposed CL offers separate control over preservation of the speech component quality, suppression of the noise component, and preservation of a naturally sounding residual noise component. We illustrate the potential of the proposed CL by evaluating a standard convolutional neural network (CNN) for mask-based speech enhancement. The new CL is compared to several baseline losses, comprising the conventional mean squared error (MSE) loss w.r.t. speech spectral amplitudes or w.r.t. an ideal-ratio mask, auditory-related loss functions, such as the perceptual evaluation of speech quality (PESQ) loss and the perceptual weighting filter loss, and also the recently proposed SNR loss with two masks. Detailed analysis suggests that the proposed CL obtains a better or at least a more balanced performance across all employed instrumental quality metrics, including SNR improvement, speech component quality, enhanced total speech quality, and particularly also delivers a natural sounding residual noise component. For unseen noise types, we excel even perceptually motivated losses by an about 0.2 points higher PESQ score. The recently proposed so-called SNR loss with two masks not only requires a network with more parameters due to the two decoder heads, but also falls behind on PESQ and POLQA and particularly w.r.t. residual noise quality. Note that the proposed CL shows significantly more 1st ranks among the evaluation metrics than any other baseline. It is easy to implement, and code is provided at https://github.com/ifnspaml/Components-Loss.

Impact of optical noises on unipolar-coded Brillouin optical time-domain analyzers.

Noise models for both single-pulse and coded Brillouin optical time-domain analyzers (BOTDA) are established to quantify the actual signal-to-noise ratio (SNR) enhancement provided by pulse coding at any fiber position and in any operating condition. Simulation and experimental results show that the polarization noise and spontaneous Brillouin scattering (SpBS) to signal beating noise could highly penalize the performance of coded-BOTDA, depending on the code type and the interrogated fiber position. The models also serve as a useful tool to optimize the SNR improvement by trading off the accumulated Brillouin gain and optical noises.

A denoising performance comparison based on ECG Signal Decomposition and local means filtering

The electrocardiogram (ECG) signal is popular and extensively used as a diagnostic tool for revealing several diseases related to the heart. Unfortunately, its sensitive to noises from various sources. Denoising these noises remains a hard and major challenge task, although there are some existing methods for denoising, they remain limited to a few types of noises with significant distortion after the filtering process. In this paper, we investigated a hybrid system that can deal with different types of noises, with different levels of SNR, conserving all the signal features with the minimum distortion. The hybrid system consists of a decomposition method followed by local means (LM) filtering. The best candidates used for the signal decomposition are the Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). To demonstrate the effectiveness of the proposed method, several real ECG recordings and synthetic ECG infected by a different type of noise (i.e., baseline wander, muscle artifacts, electrode motion, mix noise, white noise, colored noise) with different levels of signal to noise ratios (24, 18, 6, 0, −6) dB are used in our numerical analysis. The performance evaluation of our proposed system is made using the following metrics: SNR improvement, signal-to-noise and distortion ratio (SINAD), the approximate entropy, and the fuzzy entropy. The experimental results emphasized that the Ensemble Empirical Mode Decomposition (EEMD) followed by local means (LM) outperforms all the comparison methods, in terms of all the metrics used, with few exceptions where the Empirical Mode Decomposition (EMD) followed by local means (LM) and Discrete Wavelet Transform (DWT) followed by local means (LM) performed well.