Movable Electrode Research Articles

Sudden Cardiac Death Risk Prediction Based on Noise Interfered Single-Lead ECG Signals

Sudden cardiac death (SCD) represents a critical acute cardiovascular event characterized by rapid onset of cardiac and respiratory arrest, posing a significant threat to patients due to its high fatality rate. Monitoring indices related to SCD using wearable devices holds profound implications for preemptive measures aimed at reducing the incidence of such life-threatening events. Hence, this study proposed a predictive algorithm for SCD leveraging single-lead electrocardiogram (ECG) signals featuring low signal-to-noise ratios. Initially, simulated electrode motion artifact noise was introduced to ideal ECG signals to emulate the signal conditions with low signal-to-noise ratios encountered in everyday scenarios. To meet the criteria of simplicity and cost-effectiveness required for wearable devices, the analysis focused exclusively on single-lead signals. The proposed algorithm in this study employed a lightweight machine learning approach to extract 12-dimensional features encompassing ventricular late potentials, T-wave electrical alternation, and corrected QT intervals from the signal. The algorithm achieved an average prediction accuracy of 93.22% within 30 min prior to SCD onset, and 95.43% when utilizing a normal sinus rhythm database as a control, demonstrating robust performance. Additionally, a comprehensive Sudden Cardiac Death Index (SCDI) was devised to quantify the risk of SCD, formulated by integrating pivotal two-dimensional features contributing significantly to the algorithm. This index effectively distinguishes high-risk signals indicative of SCD from normal signals, thereby offering valuable supplementary insights in clinical settings.

Machining characteristics of Al–TiB2 using a combination of rotary and low-frequency vibratory motion of tool electrode

Machining characteristics of Al–TiB2 using a combination of rotary and low-frequency vibratory motion of tool electrode

Arc Ignition Methods and Combustion Characteristics of Small-Current Arc Faults in High-Voltage Cables

High-voltage cables will continue to operate for a period of time in the event of a small current arc fault, which poses a risk of fire. Two simulated ignition methods, moving electrode and melting fuses, are proposed to analyze the ignition characteristics of low-current arcs. The ignition test was carried out, and the combustion effect was compared. The results indicate that the moving electrode ignition method can achieve long-distance arc ignition test when the current is small and is suitable for simulating the arc ignition situation of cable outer protective layer damage. By controlling the movement speed, it can be ensured that the arc will not be interrupted during the electrode movement process. However, the arc is difficult to sustain using the fuse melting method when the current is small and the distance is long. The fuse melting method is suitable for simulating insulation breakdown situations. The results show that the critical arc duration for cable ignition under five different current conditions of 2–10 A is 28 s, 21 s, 14 s, 9 s, and 4 s, respectively. The maximum height of the cable flame under 2–10 A arc current is 9–52 cm and 16–63 cm, respectively, when the arc duration is 50 s and 100 s. The self-ignition time of the cable after the arc extinguishing is 8–95 s and 14–261 s, respectively. The maximum temperature of the cable flame is positively correlated with arc current, and the maximum flame temperature of the cable under 2–10 A arc current is 540–980 °C. Based on the actual current monitoring data in cable tunnels, the research results can provide reference for the risk assessment and protection of cable tunnel fires.

Motion Artifacts in Dynamic EEG Recordings: Experimental Observations, Electrical Modelling, and Design Considerations

Despite the progress in the development of innovative EEG acquisition systems, their use in dynamic applications is still limited by motion artifacts compromising the interpretation of the collected signals. Therefore, extensive research on the genesis of motion artifacts in EEG recordings is still needed to optimize existing technologies, shedding light on possible solutions to overcome the current limitations. We identified three potential sources of motion artifacts occurring at three different levels of a traditional biopotential acquisition chain: the skin-electrode interface, the connecting cables between the detection and the acquisition systems, and the electrode-amplifier system. The identified sources of motion artifacts were modelled starting from experimental observations carried out on EEG signals. Consequently, we designed customized EEG electrode systems aiming at experimentally disentangling the possible causes of motion artifacts. Both analytical and experimental observations indicated two main residual sites responsible for motion artifacts: the connecting cables between the electrodes and the amplifier and the sudden changes in electrode-skin impedance due to electrode movements. We concluded that further advancements in EEG technology should focus on the transduction stage of the biopotentials amplification chain, such as the electrode technology and its interfacing with the acquisition system.

A Circular Touch Mode Capacitive Rainfall Sensor: Analytical Solution and Numerical Design and Calibration

A circular capacitive rainfall sensor can operate from non-touch mode to touch mode; that is, under the action of enough rainwater, its movable electrode plate can form a circular contact area with its fixed electrode plate. Therefore, the weight of rainwater is borne by only its movable electrode plate in non-touch mode operation but by both its movable and fixed electrode plates in touch mode operation, and the total capacitance of its touch mode operation is much larger than that of its non-touch mode operation. Essential to its numerical design and calibration is the ability to predict the deflection shape of its moveable electrode plate to determine its total capacitance. This requires the analytical solution to the fluid–structure interaction problem of its movable electrode plate under rainwater. In our previous work, only the analytical solution for the fluid–structure interaction problem before its movable electrode plate touches its fixed electrode plate was obtained, and how to numerically design and calibrate a circular non-touch mode capacitive rainfall sensor was illustrated. In this paper, the analytical solution for the fluid–structure interaction problem after its movable electrode plate touches its fixed electrode plate is obtained, and how to numerically design and calibrate a circular touch mode capacitive rainfall sensor is illustrated for the first time. The numerical results show that the total capacitance and rainwater volume when the circular capacitive rainfall sensor operates in touch mode is indeed much larger than that when the same circular capacitive rainfall sensor operates in non-touch mode, and that the average increase in the maximum membrane stress per unit rainwater volume when the circular capacitive rainfall sensor operates in touch mode can be about 20 times smaller than that when the same circular capacitive rainfall sensor operates in non-touch mode. This is where the circular touch mode capacitive rainfall sensor excels.

Ambulatory ECG noise reduction algorithm for conditional diffusion model based on multi-kernel convolutional transformer.

Ambulatory electrocardiogram (ECG) testing plays a crucial role in the early detection, diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG signals are essential for the subsequent analysis of these conditions. However, ECG signals obtained during exercise are susceptible to various noise interferences, including electrode motion artifact, baseline wander, and muscle artifact. These interferences can blur the characteristic ECG waveforms, potentially leading to misjudgment by physicians. To suppress noise in ECG signals more effectively, this paper proposes a novel deep learning-based noise reduction method. This method enhances the diffusion model network by introducing conditional noise, designing a multi-kernel convolutional transformer network structure based on noise prediction, and integrating the diffusion model inverse process to achieve noise reduction. Experiments were conducted on the QT database and MIT-BIH Noise Stress Test Database and compared with the algorithms in other papers to verify the effectiveness of the present method. The results indicate that the proposed method achieves optimal noise reduction performance across both statistical and distance-based evaluation metrics as well as waveform visualization, surpassing eight other state-of-the-art methods. The network proposed in this paper demonstrates stable performance in addressing electrode motion artifact, baseline wander, muscle artifact, and the mixed complex noise of these three types, and it is anticipated to be applied in future noise reduction analysis of clinical dynamic ECG signals.

New insights into muscle activity associated with phantom hand movements in transhumeral amputees.

Muscle activity patterns in the residual arm are systematically present during phantom hand movements (PHM) in transhumeral amputees. However, their characteristics have not been directly investigated yet, leaving their neurophysiological origin poorly understood. This study pioneers a neurophysiological perspective in examining PHM-related muscle activity patterns by characterizing and comparing them with those in the arm, forearm, and hand muscles of control participants executing intact hand movements (IHM) of similar types. To enable rigorous comparison, we developed meta-variables independent of electrode placement, quantifying the phasic profile of recorded surface EMG signals and the specificity of their patterns across electrode sites and movement types. Similar to the forearm and hand muscles during IHM, each signal recorded from the residual upper arm during PHM displays a phasic profile, synchronized with the onset and offset of each movement repetition. Furthermore, the PHM-related patterns of phasic muscle activity are specific not only to the type of movement but also to the electrode site, even within the same upper arm muscle, while these muscles exhibit homogeneous activities in intact arms. Our results suggest the existence of peripheral reorganization, eventually leading to the emergence of independently controlled muscular sub-volumes. This reorganization potentially occurs through the sprouting of severed axons and the recapture of muscle fibers in the residual limb. Further research is imperative to comprehend this mechanism and its relationship with PHM, holding significant implications for the rehabilitation process and myoelectric prosthesis control.

Personalized computational electro-mechanics simulations to optimize cardiac resynchronization therapy.

In this study, we present a computational framework designed to evaluate virtual scenarios of cardiac resynchronization therapy (CRT) and compare their effectiveness based on relevant clinical biomarkers. Our approach involves electro-mechanical numerical simulations personalized, for patients with left bundle branch block, by means of a calibration obtained using data from Electro-Anatomical Mapping System (EAMS) measures acquired by cardiologists during the CRT procedure, as well as ventricular pressures and volumes, both obtained pre-implantation. We validate the calibration by using EAMS data coming from right pacing conditions. Three patients with fibrosis and three without are considered to explore various conditions. Our virtual scenarios consist of personalized numerical experiments, incorporating different positions of the left electrode along reconstructed epicardial veins; different locations of the right electrode; different ventriculo-ventricular delays. The aim is to offer a comprehensive tool capable of optimizing CRT efficiency for individual patients. We provide preliminary answers on optimal electrode placement and delay, by computing some relevant biomarkers such as , ejection fraction, stroke work. From our numerical experiments, we found that the latest activated segment during sinus rhythm is an effective choice for the non-fibrotic cases for the location of the left electrode. Also, our results showed that the activation of the right electrode before the left one seems to improve the CRT performance for the non-fibrotic cases. Last, we found that the CRT performance seems to improve by positioning the right electrode halfway between the base and the apex. This work is on the line of computational works for the study of CRT and introduces new features in the field, such as the presence of the epicardial veins and the movement of the right electrode. All these studies from the different research groups can in future synergistically flow together in the development of a tool which clinicians could use during the procedure to have quantitative information about the patient's propagation in different scenarios.

A Theoretical Study on Static Gas Pressure Measurement via Circular Non-Touch Mode Capacitive Pressure Sensor.

A circular non-touch mode capacitive pressure sensor can operate in both transverse and normal uniform loading modes, but the elastic behavior of its movable electrode plate is different under the two different loading modes, making its input-output analytical relationships between pressure and capacitance different. This suggests that when such a sensor operates, respectively, in transverse and normal uniform loading modes, the theory of its numerical design and calibration is different, in other words, the theory for the transverse uniform loading mode (available in the literature) cannot be used as the theory for the normal uniform loading mode (not yet available in the literature). In this paper, a circular non-touch mode capacitive pressure sensor operating in normal uniform loading mode is considered. The elastic behavior of the movable electrode plate of the sensor under normal uniform loading is analytically solved with the improved governing equations, and the improved analytical solution obtained can be used to mathematically describe the movable electrode plate with larger elastic deflections, in comparison with the existing two analytical solutions in the literature. This provides a larger technical space for developing the circular non-touch mode capacitive pressure sensors used for measuring the static gas pressure (belonging to normal uniform loading).

Retraction Note to: Design, Construction, and Installation of Movable Electrode Biasing System on Tokamak

Retraction Note to: Design, Construction, and Installation of Movable Electrode Biasing System on Tokamak

Cleaning ECG with Deep Learning: A Denoiser Tested in Industrial Settings

As the popularity of wearables continues to scale, a substantial portion of the population has now access to (self-)monitorization of cardiovascular activity. In particular, the use of ECG wearables is growing in the realm of occupational health assessment, but one common issue that is encountered is the presence of noise which hinders the reliability of the acquired data. In this work, we propose an ECG denoiser based on bidirectional Gated Recurrent Units (biGRU). This model was trained on noisy ECG samples that were created by adding noise from the MIT-BIH Noise Stress Test database to ECG samples from the PTB-XL database. The model was initially trained and tested on data corrupted with the three most common sources of noise: electrode motion artifacts, muscle activation and baseline wander. After training, the model was able to fully reconstruct previously unseen signals, achieving Root-Mean-Square Error values between 0.041 and 0.023. For further testing the model’s robustness, we performed a data collection in an industrial work setting and employed our model to clean the noisy data, acquired from 43 workers using wearable sensors. The trained network proved to be very effective in removing real ECG noise, outperforming the available open-source solutions, while having a much smaller complexity compared to state-of-the-art Deep Learning approaches.

The Research of EEG Headset of Brain–Computer Interface for Artificial Intelligence-Related Applications

Over the past decade, a wide range of brain–computer interface (BCI) applications with artificial intelligence (AI) assistance have emerged. While using a head-mounted electroencephalogram (EEG) device for BCI application, it often requires a user to wear the device close to the scalp so that clear EEG signals can be obtained from the head directly. Those signals can be used to determine a user’s mental state. Due to the head size differences between Chinese and European users, as well as the differences between Chinese users themselves, EEG headsets with fixed-electrode-point will enable some electrodes fail to capture a user’s EEG data accurately. Combined with previous research and related design methodology, the design of a head-mounted EEG device is proposed in this paper. In this study, Chinese youth are regarded as the target user group. After evaluating different designs of device structures, the possible positions and movement methods of electrodes, an EEG headset with movable electrodes for young Chinese users is finally delivered.

FPGA implementation of IIR elliptic filters for de-noising ECG signal

De-noising of ECG signal is very much necessary to monitor the heart health and to diagnose disease. This paper presents a real time de-noising system that efficiently wiped out the noises from corrupted ECG signal and improves its SNR which enhances the interpretation of features present in ECG signal. This proposed system is designed with three Infinite Impulse Response (IIR) elliptic filters in cascaded direct form II structure and is implemented on the FPGA platform. Total 376 ECG samples are taken from the open access public database physiobank. All the architectural level FPGA designs have been developed in Xilinx System Generator (XSG) and implemented on Xilinx Zynq 7000 board. Our design utilized only 3.87 % of the total available resource and consumed 0.321 watt on-chip power. Worst Negative Slack (WNS) for the critical path is 2.101 ns. The positive value of WNS signifies our hardware design implementation run successfully by meeting the required time constraint. To judge the performance of the proposed design, the measurement matrices SNR Improvement, RMSE, PRD, SSNR and SINAD are calculated and achieved 81.11% , 88.89% and 85.41% accuracy for ECG recordings of ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia database respectively. The novelty of this proposed denoising system is its robustness, which work satisfactorily for Baseline Wander (BW), Electrode Motion (EM), Power Line Interference (PLI), Muscle or Electromyography (EMG) and as well as Additive White Gaussian Noise (AWGN).

A Method of Precise Auto-Calibration in a Micro-Electro-Mechanical System Accelerometer.

A novel design of a MEMS (Micro-Electromechanical System) capacitive accelerometer fabricated by surface micromachining, with a structure enabling precise auto-calibration during operation, is presented. Precise auto-calibration was introduced to ensure more accurate acceleration measurements compared to standard designs. The standard mechanical structure of the accelerometer (seismic mass integrated with elastic suspension and movable plates coupled with fixed plates forming a system of differential sensing capacitors) was equipped with three movable detection electrodes coupled with three fixed electrodes, thus creating three atypical tunneling displacement transducers detecting three specific positions of seismic mass with high precision, enabling the auto-calibration of the accelerometer while it was being operated. Auto-calibration is carried out by recording the accelerometer indication while the seismic mass occupies a specific position, which corresponds to a known value of acting acceleration determined in a pre-calibration process. The diagram and the design of the mechanical structure of the accelerometer, the block diagram of the electronic circuits, and the mathematical relationships used for auto-calibration are presented. The results of the simulation studies related to mechanical and electric phenomena are discussed.

Performance evaluation in die-sinking EDM of polygonal micro cavities over EN-24 alloy steel using cylindrical electrode and polygon cycle approach

The integration of advanced Electrical Discharge Machining (EDM) technology, incorporating short and electronically pulsed discharges with precise circular electrode movements, has enabled the production of intricate 3D microstructures and cavities. This paper aims to investigate the machining performance of polygonal cavities—triangular, square, pentagon, and hexagon—fabricated on steel samples using a 400 µm cylindrical copper electrode within the polygon cycle approach of EDM. Experimental investigations were conducted to assess shape error, tool wear rate, recast layer formation, and elemental characterization. Various discharge patterns and side discharges were examined to understand their effects on spark gap uniformity, tool deflection, and discharge stability. The results revealed non-uniform spark gaps, tool deflection, and corner rounding due to varying discharge patterns and side discharges. Tool wear rate was found to be directly related to polygon complexity, with higher-order polygons leading to increased wear due to extended machining durations and heat accumulation. The maximum tool wear of 7.69 × 104 µm3/s occurred in case of hexagonal cavities while in case of triangular cavities, the tool wear rate was minimum having value of 5.77 × 104 µm3/s. Scanning Electron Microscopy (SEM) examinations showed the presence of recast layers and micro-cracks, particularly in cavities with higher shape complexities. Energy Dispersive X-ray Spectroscopy (EDS) analysis identified copper deposition, local material evaporation, and foreign element accumulation on the cavity surfaces. This study provides insights into the machining performance of polygonal cavities in EDM processes. The findings underscore the influence of discharge patterns, polygon complexity, and material interactions on tool wear, surface quality, and microstructure integrity. Understanding these factors can inform optimization strategies for EDM processes, leading to enhanced precision and efficiency in microstructure fabrication.

Enhancement of single-lead dry-electrode ECG through wavelet denoising

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dry-electrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG’s QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10−6 and 1.65 × 10−7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG’s QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals.

Producing two-dimensional dust clouds and clusters using a movable electrode for complex plasma and fundamental physics experiments.

We report a Bidirectional Electrode Control Arm Assembly (BECAA) for precisely manipulating dust clouds levitated above the powered electrode in RF plasmas. The reported techniques allow the creation of perfectly 2D dust layers by eliminating off-plane particles by moving the electrode from outside the plasma chamber without altering the plasma conditions. The tilting and moving of electrodes using BECAA also allows the precise and repeatable elimination of dust particles one by one to achieve any desired number of grains N without trial and error. Simultaneously acquired top and side view images of dust clusters show that they are perfectly planar or 2D. A demonstration of clusters with N = 1-28 without changing the plasma conditions is presented to show the utility of BECAA for complex plasma and statistical physics experimental design. Demonstration videos and 3D printable part files are available for easy reproduction and adaptation of this new method to repeatably produce 2D clusters in existing RF plasma chambers.

Finite Element Mode-ling of Damping of a Squeeze Gas Film in High-Frequency Microelectromechanical Switches with Capacitive Contact

The damping effects of a squeeze gas film are one of the main components of damping and naturally arise in the designs of dynamic micromechanical devices, in particular, in high-frequency micromechanical switches with an electrostatic activation mechanism, in which the electrostatic drive consists of a fixed electrode in the form of a plate and a movable electrode in the form of a movable plate suspended on elastic suspensions for the case of capacitive contact. In the static mode of the device, a thin film of gas consisting of air molecules or other inert gas surrounding the device is located and held between the electrodes of the electrostatic drive. This article presents a mathematical model based on the obtained linear inhomogeneous Reynolds differential equation and performs finite element modeling of the pressure distribution in the plate of a suspended movable electrode during compression of a gas film during electrostatic activation of micromechanical switches with a capacitive contact and parallel arrangement of the electrodes of an electrostatic drive depending on the magnitude of the coefficient of this component of damping in a three-dimensional coordinate system, the dependences between the damping force and the elastic force of the compressible gas film on the value of the damping coefficient are also presented. The presented results are applicable to assess the effect of isothermal damping of a squeeze gas film on the dynamics of micromechanical devices, in particular, on the dynamic characteristics of high-frequency micromechanical switches with an electrostatic activation mechanism and a capacitive contact.

Modeling and experiments on flexible side-electrodes electrostatic actuators: Influence of the contact interface on the available force

This paper presents an in-depth study of electrostatic actuators based on flexible side-electrodes focusing on the available electrostatic force and the derived useful force. A design tool is proposed considering technological aspects as aspect ratio, insulating layer material and voltage level. A space discretized mechanical approach based on Euler-Bernoulli beam theory is developed to model the bending deflection of the flexible movable electrode and “zipping” contact. Analytical developments and simplifications are achieved, rapidly and efficiently implemented in a proposed algorithm. ANSYS® simulations validate the procedure for updating the contact zone through the updating of the electrostatic pressure distribution. Optimum movable electrode width and appropriate “opposable” stiffness for guiding system are discussed. A “residual air thickness” parameter is introduced to address imperfect contact between the electrodes. Experimentalcharacterization studies various prototypes with different structural parameters as thickness (50 µm and 200 µm), gaps (5 µm and 15 µm) and material for insulating layers (silicon oxide and parylene C). The metrology campaign highlights the impact of aspect ratio and oxidation/deoxidation on electrode sidewall quality and hence mechanical performance.A comparison with well-known comb-drive shows higher electrostatic force for flexible side-electrode actuators. As an example, for a 10 µm stroke, the available electrostatic force for flexible electrodes with irregular sidewalls (residual air thickness 0.6 µm) is 2.2 times greater and rises to 7.3 times greater for perfect sidewalls (no residual air).

Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal

ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals.