Empirical Mode Decomposition Algorithm Research Articles

Non-contact wearable synchronous measurement method of electrocardiogram and seismocardiogram signals.

Cardiovascular disease is one of the leading threats to human lives and its fatality rate still rises gradually year by year. Driven by the development of advanced information technologies, such as big data, cloud computing, and artificial intelligence, remote/distributed cardiac healthcare is presenting a promising future. The traditional dynamic cardiac health monitoring method based on electrocardiogram (ECG) signals only has obvious deficiencies in comfortableness, informativeness, and accuracy under motion state. Therefore, a non-contact, compact, wearable, synchronous ECG and seismocardiogram (SCG) measuring system, based on a pair of capacitance coupling electrodes with ultra-high input impedance, and a high-resolution accelerometer were developed in this work, which can collect the ECG and SCG signals at the same point simultaneously through the multi-layer cloth. Meanwhile, the driven right leg electrode for ECG measurement is replaced by the AgCl fabric sewn to the outside of the cloth for realizing the total gel-free ECG measurement. Besides, synchronous ECG and SCG signals at multiple points on the chest surface were measured, and the recommended measuring points were given by their amplitude characteristics and the timing sequence correspondence analysis. Finally, the empirical mode decomposition algorithm was used to adaptively filter the motion artifacts within the ECG and SCG signals for measuring performance enhancement under motion states. The results demonstrate that the proposed non-contact, wearable cardiac health monitoring system can effectively collect ECG and SCG synchronously under various measuring situations.

A Study of Prediction of Ground Settlement of Shield Tunnel Based on EEMD-BO-GRU Algorithm

Because the finite element method cannot meet the needs of shield tunnel engineering, multiple linear regression and traditional machine learning algorithms are difficult to effectively solve the shield tunnel’s nonlinear and non-stationary ground surface settlement data. Based on the Gated Recurrent Unit (GRU) algorithm of Ensemble empirical mode decomposition (EEMD) and Bayesian optimization (BO), this paper puts forward a prediction method of ground surface settlement during shield tunneling. Taking the shield tunnel construction of Zhijiang Road in Hangzhou as an example, by comparing with the traditional machine learning algorithm, the results show that RMSE, MAE, and MAPE of this algorithm are increased by 1.9313 mm, 1.7526 mm, and 4.9553%. It can be explained that this method has higher prediction accuracy and a more stable prediction effect, and the EEMD algorithm can effectively improve the generalization ability of the algorithm.

Spectrum‐based fault localization using empirical mode decomposition algorithm

AbstractSpectrum‐based fault localization (SBFL) is considered as the most popular lightweight fault localization method. However, pure SBFL is proved to be tedious and time‐consuming for programmers to detect faults. This is because the suspiciousness is duplicated and they usually involve only the first few suspicious elements in the debugging process before losing patience. For this reason, benefiting from abundant spectrum information created by SBFL, we propose a new spectral fault localization technique using empirical mode decomposition method (EMD) to improve the accuracy of automatic software debugging. To accomplish that, the faulty program is evaluated by SBFL, and then, the resultant suspiciousness scores are taken as signals and nonfaulty elements as noise. Next, EMD is employed to decompose the suspicious scores of SBFL to eliminate massively repeated ones. Hence, elements are reranked according to new scores, and the ranking list is reconstructed by enlarging the high‐performance range (checking 5% elements) and TOP‐5 region to detect more faults. The performance of EMD‐SBFL is tested and compared with pure SBFL with EXAM scores and Top‐n ranks both on Siemens programs with seeded faults and large‐sized Defects4j programs with real faults. The result reveals that EMD‐SBFL is significantly effective to locate nearly over doubled faults by only check Top‐1 and outperforms the state‐of‐the art SBFL techniques.

An adaptive infrared image denoising method based on two-dimensional empirical mode decomposition for distribution network inspection UAV

An adaptive denoising method based on 2D empirical mode decomposition (EMD) is proposed to improve the infrared image quality of inspection UNMANNED aerial vehicles (UAVs) and provide guarantee for improving the inspection level of distribution network. Through rapid adaptive two-dimensional empirical mode decomposition algorithm decomposition of a UAV collected for distribution network inspection original noise of infrared image, get more than the IMF component and the residual amount, a forecast noise dominated the IMF component parameters such as threshold value and the variance of noise, using the estimated parameters in combination with the optimal linear interpolation algorithm of noise threshold function of leading the IMF component implementation of threshold denoising. After the denoised IMF component is obtained, the denoised infrared image is obtained after reconstruction with the signal-dominated IMF component, and the adaptive denoising of the infrared image of the distribution network inspection UAV is realized. The experimental results show that the method in this paper can maintain the details of the image, improve the definition, significantly improve the visual effect, the overall denoising performance is stable and feasible, and ensure the quality inspection UAV to collect infrared images.

A hybrid data-driven framework for satellite telemetry data anomaly detection

Anomaly detection based on telemetry data is a prominent health condition monitoring task that plays an increasingly crucial role in identifying unexpected events and improving overall satellite reliability. In this paper, a new hybrid data-driven framework for satellite telemetry data anomaly detection is constructed by an integration of ensemble empirical mode decomposition with complexity deviation index and grey wolf optimizer-optimized support vector machine. Initially, the ensemble empirical mode decomposition algorithm is employed to decompose the raw telemetry data into a collection of intrinsic mode functions that represent different inherent oscillatory modes or characteristics. A critical intrinsic mode function selection method, i.e., the complexity deviation index method, is proposed based on the sample entropy theory to select the most appropriate intrinsic mode functions with the goal of improving the sufficiency of the ensemble empirical mode decomposition algorithm and enhancing the anomalous characteristics of the telemetry data. Subsequently, a grey wolf optimizer-optimized support vector machine model is developed to forecast the variation tendency of the synthesized series of the selected intrinsic mode functions. The prediction residual between the ground truth and model estimator is calculated and the optimal threshold for anomaly detection is set according to the principle of maximum Youden index. Finally, the proposed method is applied to analyze and detect anomalies in telemetry data collected from a real-world satellite. The experimental results verify the capability of the proposed method in anomaly detection for satellite telemetry data.

Electricity Load and Price Forecasting Using a Hybrid Method Based Bidirectional Long Short-Term Memory with Attention Mechanism Model

The accuracy of forecasting short- or medium-term electricity loads and prices is critical in the energy market. The amplitude and duration of abnormally high prices and load spikes can be detrimental to retailers and production systems. Therefore, predicting these spikes to effectively manage risk is critical. In this paper, a novel hybrid method that combines ensemble empirical mode decomposition (EEMD) algorithm and a bidirectional long short-term memory with attention mechanism (BiLSTM-AM) model is proposed to predict electricity loads and prices. A simple approach is proposed to determine the number of intrinsic mode functions (IMFs) that decompose raw data using EEMD to avoid overdecomposition, irrelevant components, and high computational cost. Each selected mode is then modeled with BiLSTM-AM to obtain a predicted sequence. These sequences are summed and then reverted to obtain the final predicted value. The proposed method is validated using two datasets (PMJ and Australian Energy Market Operator) with different time intervals to demonstrate the generality and robustness of the forecasts, especially in temporal valley or peak forecasting. The results show that the proposed method outperforms other methods in prediction accuracy and spike-capturing ability, with EEMD reducing the mean absolute percentage error (MAPE) by 53%, 54%, and 60%, respectively. In the three forecast periods, the average MAPE and R ̲ 2 are 0.097 and 0.92, respectively. Furthermore, we use Kolmogorov-Smirnov predictive accuracy (KSPA) test and model confidence set (MCS) test to validate the superiority of the proposed model. The results demonstrate its suitability, reliability, and performance in short- and medium-term forecasting.

A Portable Microwave Intracranial Hemorrhage Imaging System Based on PSO-MCKD-CEEMDAN Method

A novel signal processing method to directly extract the hemorrhage response signals using the maximum correlation kurtosis deconvolution (MCKD) technique and complete ensemble empirical mode decomposition (CEEMDAN) algorithm is proposed in this article. This method only needs to acquire the raw data measured one time, which saves a lot of time in data acquisition. In emergency cases, a shorter time in detection means a better prognosis and better life for the patients. A portable microwave brain hemorrhage imaging system comprising a portable vector network analyzer (VNA), a small-sized antenna array (30 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 30.5 mm of each antenna), a flexible substrate, simple structure, and wideband (0.5–6 GHz) is established by applying this proposed signal processing method. The preliminary results from the experiments on the head phantom demonstrate the capability and effectiveness of this proposed method. Final results show that the proposed portable microwave imaging system can successfully detect brain hemorrhage with a size of 17 mm in diameter. The robustness of the proposed method is verified by successfully detecting the brain hemorrhage with a size of 20 mm in diameter.

Uji Kinerja Sistem Denoising Sinyal Jantung atau EKG dengan Menggunakan Algoritma Empirical Mode Decomposition (EMD)

Electrocardiograms (EKGs) are signals created by the electrical activity of the heart muscle and displayed on the EKG device's monitor. Using the EKG recording, the primary characteristics for diagnosing the status of the human heart can be determined. The death rate of heart patients can be reduced through early identification of cardiac problems. In ECG readings, it is frequently affected by several disruptions caused by muscle contractions and electrode movement. Numerous investigations on ECG signal denoising techniques have been undertaken earlier. This article examines the testing of the performance of EKGs using the denoising technique based on the Empirical Mode Decomposition (EMD) algorithm. In this work, many metrics were utilized to evaluate the ECG signal denoising technique: mean square error (MSE), mean absolute error (MAE), and signal-to-noise ratio (SNR). In this investigation, the ECG data was contaminated with noise from muscle artifacts (MA), additive Gaussian white noise (AWGN), electrode movement (EM), and baseline wander (BW). The noise-contaminated ECG signal is subsequently subjected to the denoising process. Calculate the MSE, MAE, and SNR values of the signal after it has been denoised. This study includes a scenario for testing three thresholding techniques with four distinct types of noise. The performance of the hard thresholding method is superior for all types of noise. MSE is produced by AWGN, which is 0.15, 0.28, and 9.9 dB. MA noise generates MSE, MAE, and SNR values of 0.4, 0.033, and 41.0 dB, respectively. The EM noise has an MSE of 0.010, an MAE of 0.04, and an SNR of 30.8 dB. The MSE produced by BW noise is 0.008; the MAE and SNR values were 0.0356 and 28.5, respectively.

Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

AbstractThe echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.

Fault Diagnosis of Rolling Bearing Based on ICEEMDAN and SSA-RVM

In order to effectively improve the fault identification rate of correlation vector machine (RVM) in bearing fault model diagnosis and identification, a rolling bearing troubleshooting method improvement-based adaptive white noise complete integrated empirical mode decomposition (ICEEMDAN) and sparrow search algorithm (SSA) optimized correlation vector machine (RVM) is proposed. A rolling bearing troubleshooting method improvement-based adaptive white noise complete integrated empirical mode decomposition (ICEEMDAN) and sparrow search algorithm (SSA) optimized correlation vector machine (RVM) was presented. First, the ICEEMDAN method is used to decompose the raw vibration signal, and the power entropy and the proportion of the power of the intrinsic mode component (IMF) to the total power of the signal are taken as the fault characteristics. Optimization of the width factor and hyperparameters of RVM using SSA algorithm, and the RVM model is built to realize fault Identification of rolling bearings. The test results indicate that the SSA-RVM model has a high fault identification rate and dramatically improves the accuracy of fault diagnosis.

Extraction of Strain Characteristic Signals from Wind Turbine Blades Based on EEMD-WT

Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade, so as to ensure the safe and stable operation of wind turbine in natural environment. The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics. The strain signal contains a lot of noise, which makes the analysis error. Therefore, it is very important to denoise and extract features of measured signals before signal analysis. In this paper, the joint algorithm of ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is used for the first time to achieve sufficient noise reduction and effectively extract the feature signals of non-stationary strain signals. The application process of EEMD-WT is optimized. This optimization can avoid the repeated selection of wavelet basis function and the number of decomposition layers due to different crosswind conditions. EEMD adaptively decomposes the strain signal into intrinsic mode functions, to judge the frequency of IMFs, remove the high-frequency noise components, retain the useful components. The useful components are denoised twice by the wavelet transform, the components and residual terms after the secondary denoising are reconstructed to obtain the characteristic signal. The EEMD-WT was applied to process the simulating signals and measured the strain signals. The results were compared with the results of the EEMD. The results showed that the EEMD-WT method has better noise reduction performance, and can effectively extract the characteristics of strain signals, which lays a solid foundation for accurate analysis of wind turbine blade strain signals under crosswind conditions.

A New Approach to Empirical Mode Decomposition Based on Akima Spline Interpolation Technique

The objective of this research work is to extend the scope of empirical mode decomposition (EMD) algorithm, as an efficient tool to decompose the nonlinear and non-stationary time series. For EMD to be widely applicable, the extension utilizes both clean and noisy data sets. When constructing upper and lower envelopes, the proposed algorithm utilizes the Akima spline interpolation technique rather than a cubic spline. The proposed EMD is called Akima-EMD, which is used to identify non-informative fluctuations in the signal, such as noise, outliers, and ultra-high frequency components, and to breakdown the clean and chaotic data into various components avoiding distortion. It has been shown through the synthetic as well as real-world time series data analysis that the proposed method successfully extracts noise in the form of the first IMF from the data.

Integrated S-Transform-Based Learning System for Detection of Arrhythmic Fetus

Measurement of abnormal heartbeat rhythm of a fetus to detect arrhythmia using fetal-electrocardiogram (f-ECG) signals is one of the most convenient methods, used to quickly assess the health status of the fetus. However, instruments used to record the clean f-ECG under free-living conditions have several operational difficulties. Therefore, this study presents a new integrated Stock-well transform-based learning system to evacuate the clean f-ECG signals from the mother’s abdomen and automatically detect the arrhythmic fetuses. In this work, an empirical mode decomposition (EMD) algorithm is employed to decompose the raw signals into various intrinsic mode functions (IMFs) and a correlation criterion is set to eliminate noisy IMFs. The remaining noise-free IMFs are utilized to reconstruct the de-noised f-ECG signals. Stock-well transform is used to convert the clean f-ECG signals into time-frequency (T-F) images. Obtained images are given to two pre-built Alex-Net and Squeeze-Net along with a newly developed lightweight deep convolutional model (DCM). The presented system has yielded an average detection accuracy of 95.31%, an area under the curve of 99.57, and a specificity of 96.29%. The comparison results depict that the developed system provides higher detection performance compared to the state-of-the-art techniques. The proposed learning system efficiently detected the arrhythmic fetus, which may enhance medical measurement performance by reducing operational cost/difficulties of various f-ECG recording instruments, and can assist obstetricians in deciding on delivery. Our presented system is suitable to design abdominal wearables for continuous monitoring of the fetus.

Research on water quality prediction based on PE-CNN-GRU hybrid model

Sewage treatment is a complex and nonlinear process. In this paper, a prediction method based on convolutional neural network (CNN) and gated recurrent unit (GRU) hybrid neural network is proposed for the prediction of dissolved oxygen concentration in sewage treatment. Firstly, akima 's method is used to complete the filling preprocessing of missing data, and then the integrated empirical mode decomposition (EEMD) algorithm is used to denoise the key factors of water quality data. Pearson correlation analysis is used to select better water quality parameters as the input of the model. Then, CNN is used to convolve the data sequence to extract the feature components of sewage data. Then, the CNN-GRU hybrid network is used to extract the feature components for sequence prediction, and then the predicted output value is obtained. The mean absolute error (MAE), root mean square error (RMSE) and mean square error (MSE) were used as evaluation criteria to analyze the prediction results of the model. By comparing with RNN model, LSTM model, GRU model and CNN-LSTM model, the results show that the PCA-EEMD-CNN-GRU (PE-CNN-GRU) hybrid model proposed in this paper has significantly improved the prediction accuracy of dissolved oxygen concentration.

Elastic Net Penalized Quantile Regression Model and Empirical Mode Decomposition for Improving the Accuracy of the Model Selection

In quantile regression models, numerous penalization methods have been developed to deal with ordinary least-squares method problems. Such methods are ridge penalized quantile regression, lasso penalized quantile regression, and elastic net penalized quantile regression which are used for variable selection and regularization and deals with the multicollinearity problem when it exists between the predictor variables. However, the variables of interest are often represented through time series processes, in which such time series data are often non-stationary and non-linear, which leads to poor accuracy of the resultant regression models and hence results with less reliability. The EMD-EnetQR method is proposed to address this issue, which consists of applying the empirical mode decomposition (EMD) algorithm to time series data and then using the resulting components in penalized quantile regression models. This study aims to apply the proposed EMD-QREnet method to determine the influence of the decomposition components of the original time series predictor variables on the response variable to build a model fit and improve prediction accuracy. Furthermore, this study addressed the multicollinearity between the decomposition components. Simulation studies and real dataset applications were conducted. The results show that the proposed EMD-QREnet method, in most cases, outperforms the other methods by improving prediction accuracy.

A strategy for predicting waste production and planning recycling paths in e-logistics based on improved EMD-LSTM.

With the rapid development of e-commerce, express delivery has been chosen and accepted by consumers, and a large number of express packages have resulted in serious waste of resources and environmental pollution. Because of the irregularity of online goods purchases by users in real life, logistics parks are unable to accurately judge the recycling needs of various regions. In order to solve this problem, we propose an improved empirical mode decomposition (IEMD) algorithm combined with a long-short-term memory (LSTM) network to deal with the addresses and categories in logistics data, analyze the distribution of recyclable logistics waste in the logistics park service area and in the express recycling station within the logistics park, judge the value of recyclable logistics waste, optimize the best path for recycling vehicles and improve the success rate of logistics waste recycling. In order to better research and verify the IEMD-LSTM prediction model, we model and simulate the algorithm behavior of the express waste packaging recycling prediction model system, and compare it with other classification methods through specific logistics data experiments. The prediction accuracy, stability and advantages of the four algorithms are analyzed and compared, and the application reliability of the algorithm proposed in this paper to the logistics waste recycling process is verified. The application in the actual express logistics packaging recycling case shows the feasibility and effectiveness of the waste recycling scheme proposed in this paper.

Ecological engineering induced carbon sinks shifting from decreasing to increasing during 1981–2019 in China

Substantial evidence shows that most of China's terrestrial ecosystems are important carbon sinks. However, the nonlinear trend of the carbon sinks and their nonlinear response to driving factors are unclear. Taking the net ecosystem productivity (NEP) as a proxy for the ecosystem carbon sink, the nonlinear relationships between the monotonically increasing trends and decreasing to increasing shifts in the carbon sink to climate change and ecological engineering were investigated based on ensemble empirical mode decomposition (EEMD) and machine learning algorithm (boosted regression tree model, BRT). The results suggest that 16.75 % of the carbon sinks in China experienced a monotonic increase. Additionally, 20.55 % of the carbon sinks shifted from decreasing to increasing trends, primarily after 1995, and these carbon sinks were located in the key ecological engineering areas, such as the middle reaches of the Yellow River shelterbelt program area, the Liaohe shelterbelt program area, the Grain to Green program area, and the Three-North Forest shelterbelt program area. Moreover, carbon sinks exhibited strong spatial autocorrelation with low-low clustering in the north and high-high clustering in the south. The increase in CO2 (slope of CO2 < 1.8 g/m2/s/y) and solar radiation (slope of radiation >1 w/m2/y) promoted the monotonic increase in the carbon sinks in the center of China. The increase in the areas of forest and grassland shifted the carbon sink trend from decreasing to increasing in the key ecological engineering program areas, and economic development reversed the carbon sink reduction in the Pearl River shelterbelt program area. These findings highlight the positive effect of ecological engineering on carbon sinks and provide adaptation strategies and guidance for China to achieve the “carbon neutrality” target.

Multi-step prediction of main pump leakage in nuclear power plants with an additive model

With the global demand of clean and low-carbon emission energy sources, safety in the nuclear power industry has gained widespread attention. The accurate and timely long-term fault prognostics of critical equipment in nuclear power plants (NPPs) helps to effectively schedule maintenance, thus reducing operation and maintenance costs while ensuring safety. Unpredictable future load and operations make long-term prediction of time series data in NPPs quite challenging. In this study, we proposed a framework for multi-step prediction based on an empirical mode decomposition (EMD) algorithm, which decomposed complex time series data into several components with simple trends. The useful signal and noise in the components were then divided by a relative ratio method. Candidate prediction models from the model library were trained and tuned on the signal-dominated components. The appropriate model was selected for prediction according to the characteristics of each component signal, and the prediction results were summed and smoothed with an additive model. The proposed method, noted as the denoised EMD method using an additive method (EMD-AM), was applied to the leakage prediction at shaft seals in the reactor coolant pump (RCP) of five different NPPs. Root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) were selected to determine the prediction accuracy and uncertainty. Compared with EMD-AM and EMD method using long short-term memory (EMD-LSTM), the experimental results show that the denoised EMD-AM has the overall advantage of simplicity and accuracy.

Fault Identification of Double-circuit Transmission Lines on The Same Tower Based on CEEMDAN-SSA-SVM

In order to improve the reliability of double-circuit transmission lines on the same tower, by analyzing the variation law of inverse wave of fault voltage inside and outside the area, a support vector machine (SVM) method for internal and external fault identification of double-circuit transmission lines on the same tower based on adaptive noise complete ensemble empirical mode decomposition and sparrow search algorithm optimization is proposed. This method carries on the phase mode transformation of the current and voltage after the fault. The inverse wave current in the time window after the fault is adaptively decomposed by CEEMDAN, and the corresponding energy entropy is extracted to form the feature vector. The SMOTE algorithm is used to balance the fault samples outside the region. Finally, the feature vector set is input to the SSA-SVM classifier for training and testing. Through theoretical and simulation analysis, the following results are obtained: the algorithm in this paper is not affected by fault initial angle, transition resistance and fault distance.

ECG Denoising Method Based on an Improved VMD Algorithm

Electrocardiogram (ECG) acquisition is easily contaminated by interferences, and denoising is the most important task in ECG detection. The variational mode decomposition (VMD) algorithm is widely used in ECG denoising, which can overcome mode aliasing between intrinsic mode function (IMF) components that existed in the traditional empirical mode decomposition (EMD) algorithm, but the mode decomposition number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> and penalty factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> in VMD must be optimized to obtain the best signal decomposition accuracy. This article proposes an improved VMD denoising algorithm that overcomes the shortcomings of slow parameter selection and poor generalization in the traditional VMD algorithm. The algorithm presented first adopts the EMD algorithm to remove the low-frequency baseline drift noise and then employs the adaptive particle swarm optimization (APSO) algorithm to optimize the parameter pair ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> ) for VMD. To validate the denoising performance of the improved VMD algorithm, the No. 103 record from the Massachusetts Institute of Technology (MIT) arrhythmia database is first selected as the pure ECG signal, then both 20-dB Gaussian white noises and 0.3-Hz baseline drift are added to simulate the noisy ECG signal. Second, the ECG signals of nine subjects are collected by a customized ECG detection platform based on AD8232 and ADALM1000. The ECG denoising results in simulation and actual experiments show that the improved VMD algorithm achieves the highest signal-to-noise ratio (SNR), correlation coefficient (CC), and minimum mean square error (MSE) compared with the traditional EMD and VMD algorithms, which demonstrates that the proposed denoising algorithm has stronger denoising ability and better retains morphological characteristics of the original ECG signals.