Empirical Mode Decomposition Method Research Articles

Truncation thresholds based empirical mode decomposition approach for classification performance of motor imagery BCI systems

Electroencephalogram (EEG) signals classification, which are important for brain computer interfaces (BCI) systems, is extremely difficult due to the inherent complexity and tendency to artifact properties of the signals. In this paper, a novel methodology based on Truncation Thresholds (TT) method based Empirical Mode Decomposition (EMD) method and statistical Common Spatial Pattern (CSP) feature extraction method is proposed to classified left and right hand imaginary movements from EEG signals. The TT method is used to change the selected local maximum and minimum points with EMD to distinguish more accurately the hidden information about the motor imagery cover the sub-bands in the frequency domain in addition to remove the blinking electrooculography (EOG) artefacts. TT method is performed to raw EEG signals. Then, statistical spatial features are extracted with CSP method from each Intrinsic Modal Component (IMF) which is created by used the EEG signals with the EMD method. Finally, the extracted features are fed to three different classifiers which are SVM, KNN and LDA. The proposed methodology is applied to our dataset and public BCI Competition IV-2b dataset. The results show that the proposed methodology provides accuracy of 97% and 94% with using LDA classifier for our dataset and with using KNN classifier for BCI Competition IV-2b dataset, respectively.

Research on Load Disturbance Based Variable Speed PID Control and a Novel Denoising Method Based Effect Evaluation of HST for Agricultural Machinery

This paper aims to realize and improve the constant speed control performance of tractors with HST (Hydrostatic Transmission) variable speed units. To achieve this, based on the HST test bench of the tractor, we perform a verification test of the adjustable speed characteristics, a denoising filter test of the response signal, a test on the influence of the load disturbance on the adjustable speed characteristics and a PID-based constant speed performance detection test. The results of the verification test of the adjustable speed characteristics show that the theoretical value and actual value of the adjustable speed transmission characteristics of the HST used are essentially consistent with each other. The results of the test of the load disturbance’s influence on the adjustable speed characteristics show that the increase in load torque inhibits the HST output response. Therefore, the paper proposes and designs a PID-based closed-loop constant speed control system. The paper uses a step response test and a load disturbance test to research the control result of the constant speed system. Collecting and analyzing all test results, we find that the constant speed control based on PID has a very good result. The average error between the average HST output speed and the target speed set was 0.37%, and the average standard deviation of output speed was 1.18 rpm. In addition, the paper proposes a denoising method combing the empirical mode decomposition method and the Gaussian distribution determination. The method shows that the first two orders of the components of the HST response signal should be removed as noise. The paper uses the denoised signal and the partial least squares method to analyze the influencing factors of the constant speed control result. The analysis results show that the rate of change of load torque has the biggest influence on the stability of HST output speed, followed by the target value.

Nonlinear resonance decomposition for weak signal detection.

This paper attempts to investigate the behaviors of coupling stochastic resonance (CSR) subject to α-stable noise and a periodic signal by using the residence-time ratio. Then, a nonlinear resonance decomposition is designed to successfully enhance and detect weak unknown multi-frequency signals embedded in strong α-stable noise by decomposing the noisy signal into a series of useful resonant components and a residue, where the residence-time ratio, instead of the output signal-to-noise ratio and other objective functions depending on the prior knowledge of the signals to be detected, can optimize the CSR to enhance weak unknown signals. Finally, the nonlinear resonance decomposition is used to process the raw vibration signal of rotating machinery. It is found that the nonlinear resonance decomposition is able to decompose the weak characteristic signal and its harmonics, identifying the imbalance fault of the rotor. Even the proposed method is superior to the empirical mode decomposition method in this experiment. This research is helpful to designthe noise enhanced signal decomposition techniques by harvesting the energy of noise to enhance and decompose the useful resonant components from a nonstationary and nonlinear signal.

The extraction of informative vibration components of bearings by different adaptive methods

It was suggested to select the best adaptive method after proper comparative researches, for the extraction of informative vibration components of bearings. The description and drawbacks of empirical mode decomposition method were presented, and the properties of improved ensemble empirical mode decomposition method and complete ensemble empirical mode decomposition with adaptive noise method were highlighted. A simulated additive signal contained impulse, modulation components and two sinusoids. The extracted intrinsic mode functions were the decomposition results of the first two adaptive methods, which failed to separate impulse and modulation components. Meanwhile, the intrinsic mode functions of the third adaptive method had separately impulse and modulation components, and the method proved to be effective in the separation of the vibration components during the vibrodiagnostics of bearings and gearboxes of the industrial equipment.

Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data; Part II - Nonlinear system identification

The dynamic responses of assembled structures are greatly affected by the mechanical joints, which are often the cause of nonlinear behavior. To better understand and, in the future, tailor the nonlinearities, accurate methods are needed to characterize the dynamic properties of jointed structures. In this paper, the nonlinear characteristics of a jointed beam is studied with the help of multiple identification methods, including the Hilbert Transform method, Peak Finding and Fitting method, Dynamic Mode Decomposition method, State-Space Spectral Submanifold, and Wavelet-Bounded Empirical Mode Decomposition method. The nonlinearities are identified by the responses that are measured via accelerometers in a series of experiments that consist of hammer testing, shaker ringdown testing, and response/force-control stepped sine testing. In addition to accelerometers, two high-speed cameras are used to capture the motion of the whole structure during the shaker ringdown testing. Digital Image Correlation (DIC) is then adopted to obtain the displacement responses and used to determine the mode shapes of the jointed beam. The accuracy of the DIC data is validated by the comparison between the identification results of acceleration and displacement signals. As enabled by full-field data, the energy-dependent characteristics of the structure are also presented. The setup of the different experiments is described in detail in Part I (Chen et al., 2021) of this research. The focus of this paper is to compare nonlinear system identification methods applied to different measurement techniques and to exploit the use of high spatial resolution data.

A hybrid model for multi-step wind speed forecasting based on secondary decomposition, deep learning, and error correction algorithms

Accurate and stable wind speed forecasting is an essential means to ensure the safe and stable operation of wind power integration. Therefore, a new hybrid model was proposed to improve wind speed forecasting performance, consisting of data pre-processing, model forecasting, and error correction (EC). The specific modeling process is as follows: (a) A wind speed series was decomposed into a series of subseries with different frequencies utilizing the ensemble empirical mode decomposition (EEMD) method. Afterward, various subseries were divided into high-frequency components, intermediate-frequency components, and low-frequency components based on their sample entropies (SE). (b) Three frequency components were forecast by separately employing the hybrid model of convolutional neural network and long short-term memory network (CNN-LSTM), long short-term memory network (LSTM), and Elman neural network. (c) Subsequently, an error sequence was further forecast using CNN-LSTM. (d) Finally, three actual datasets were used to forecast the multi-step wind speed, and the forecasting performance of the proposed model was verified. The test results show that the forecasting performance of the proposed model is better than the other 13 models in three actual datasets.

Serial-EMD: Fast empirical mode decomposition method for multi-dimensional signals based on serialization

Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase in amount of data puts forward higher requirements for the capability of real-time signal analysis, it is difficult for existing EMD and its variants to trade off the growth of data dimension and the speed of signal analysis. In order to decompose multi-dimensional signals at a faster speed, we present a novel signal-serialization method (serial-EMD), which concatenates multi-variate or multi-dimensional signals into a one-dimensional signal and uses various one-dimensional EMD algorithms to decompose it. To verify the effects of the proposed method, synthetic multi-variate time series, artificial 2D images with various textures and real-world facial images are tested. Compared with existing multi-EMD algorithms, the decomposition time becomes significantly reduced. In addition, the results of facial recognition with Intrinsic Mode Functions (IMFs) extracted using our method can achieve a higher accuracy than those obtained by existing multi-EMD algorithms, which demonstrates the superior performance of our method in terms of the quality of IMFs. Furthermore, this method can provide a new perspective to optimize the existing EMD algorithms, that is, transforming the structure of the input signal rather than being constrained by developing envelope computation techniques or signal decomposition methods. In summary, the study suggests that the serial-EMD technique is a highly competitive and fast alternative for multi-dimensional signal analysis.

Advanced Fusion and Empirical Mode Decomposition-Based Filtering Methods for Breathing Rate Estimation from Seismocardiogram Signals

Breathing Rate (BR), an important deterioration indicator, has been widely neglected in hospitals due to the requirement of invasive procedures and the need for skilled nurses to be measured. On the other hand, biomedical signals such as Seismocardiography (SCG), which measures heart vibrations transmitted to the chest-wall, can be used as a non-invasive technique to estimate the BR. This makes SCG signals a highly appealing way for estimating the BR. As such, this work proposes three novel methods for extracting the BR from SCG signals. The first method is based on extracting respiration-dependent features such as the fundamental heart sound components, S1 and S2 from the SCG signal. The second novel method investigates for the first time the use of data driven methods such as the Empirical Mode Decomposition (EMD) method to identify the respiratory component from an SCG signal. Finally, the third advanced method is based on fusing frequency information from the respiration signals that result from the aforementioned proposed methods and other standard methods. The developed methods in this paper are then evaluated on adult recordings from the combined measurement of ECG, the Breathing and Seismocardiograms database. Both fusion and EMD filter-based methods outperformed the individual methods, giving a mean absolute error of 1.5 breaths per minute, using a one-minute window of data.

Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery

Predictive maintenance of lithium-ion batteries has been one of the popular research subjects in recent years. Lithium-ion batteries can be used as the energy supply for industrial equipment, such as automated guided vehicles and battery electric vehicles. Predictive maintenance plays an important role in the application of smart manufacturing. This mechanism can provide different levels of pre-diagnosis for machines or components. Remaining useful life (RUL) prediction is crucial for the implementation of predictive maintenance strategies. RUL refers to the estimated useful life remaining before the machine cannot operate after a certain period of operation. This study develops a hybrid data science model based on empirical mode decomposition (EMD), grey relational analysis (GRA), and deep recurrent neural networks (RNN) for the RUL prediction of lithium-ion batteries. The EMD and GRA methods are first adopted to extract the characteristics of time series data. Then, various deep RNNs, including vanilla RNN, gated recurrent unit, long short-term memory network (LSTM), and bidirectional LSTM, are established to forecast state of health (SOH) and the RUL of lithium-ion batteries. Bayesian optimization is also used to find the best hyperparameters of deep RNNs. Experimental results with the lithium-ion batteries data of NASA Ames Prognostics Data Repository show that the proposed hybrid data science model can accurately predict the SOH and RUL of lithium-ion batteries. The LSTM network has the optimal results. The proposed hybrid data science model with multiple artificial intelligence-based technologies also demonstrates critical digital-technology enablers for digital transformation of smart manufacturing and transportation.

Micro-Motion Parameter Extraction for Ballistic Missile with Wideband Radar Using Improved Ensemble EMD Method

Micro-motion parameters extraction is crucial in recognizing ballistic missiles with a wideband radar. It is known that the phase-derived range (PDR) method can provide a sub-wavelength level accuracy. However, it is sensitive and unstable when the signal-to-noise ratio (SNR) is low. In this paper, an improved PDR method is proposed to reduce the impacts of low SNRs. First, the high range resolution profile (HRRP) is divided into a series of segments so that each segment contains a single scattering point. Then, the peak values of each segment are viewed as non-stationary signals, which are further decomposed into a series of intrinsic mode functions (IMFs) with different energy, using the ensemble empirical mode decomposition with the complementary adaptive noise (EEMDCAN) method. In the EEMDCAN decomposition, positive and negative adaptive noise pairs are added to each IMF layer to effectively eliminate the mode-mixing phenomenon that exists in the original empirical mode decomposition (EMD) method. An energy threshold is designed to select proper IMFs to reconstruct the envelop for high estimation accuracy and low noise effects. Finally, the least-square algorithm is used to do the ambiguous phases unwrapping to obtain the micro-curve, which can be further used to estimate the micro-motion parameters of the warhead. Simulation results show that the proposed method performs well with SNR at −5 dB with an accuracy level of sub-wavelength.

Automated diagnosis of muscle diseases from EMG signals using empirical mode decomposition based method

Muscle activity decreases due to various conditions like age factors and muscle diseases namely, amyotrophic lateral sclerosis (ALS) and myopathy. Electromyogram (EMG) signals are regularly explored by specialists to analyze the irregularity of muscles. Manual investigation of EMG signals is a tedious task for medical practitioners. Therefore, this work proposes a new method for classifying the ALS, myopathy, and normal EMG signals. In the proposed method, the empirical mode decomposition (EMD) method is applied to decompose the EMG signals into intrinsic mode functions (IMFs). The suitable IMFs for feature selection are selected using the t-test based approach and used to compute the foot distances denoted as fp1 and fp2 by constructing the complex plane plot. The quadrilateral is drawn over a complex plot by considering fp1 and fp2 as a diagonal of it, followed by calculating the area (A) and circumference (CF) of the quadrilateral. These measures are utilized for separating the three classes of myopathy, ALS, and normal EMG signals. The proposed algorithm has been trained and validated using a feed forward neural network (FFNN), support vector machine (SVM), and decision tree. The algorithm, when tested with a FFNN, achieved the maximum classification accuracy, sensitivity, and specificity of 99.53%, 99.25% and 99.60%, respectively.

Diagnosis of Friction on an Unbalanced Rotor by Phase-Shift Empirical Mode Decomposition Integration and Recurrence Plot

The friction and imbalance of components in rotating machines are some of the most recurrent failures that significantly increase vibration levels, thus affecting the reliability of the devices, the shelf life of its elements, and the quality of the product. There are many publications related to the different techniques for the diagnosis of friction and imbalance. In this paper, an alternative and new phase-shift empirical mode decomposition integration (PSEMDI) method is proposed to transform the acceleration into its velocity and displacement in order to construct the phase plane and recurrence plot (RP) and analyze the friction. The focus of PSEMDI and RP is to analyze nonlinear failures in mechanical systems. In machinery fault diagnosis, the main reason for using RP is to solve the integration of acceleration, and this can be achieved by phase-shifting the intrinsic mode function (IMF) with the empirical mode decomposition (EMD). Although the highest IMFs contain some frequencies, most of them have very few; thus, by applying the phase shift identity, the integration can be carried out maintaining the nonlinearities. The proposed method is compared with Simpson’s integration and detrending with the EMD method (here referred to as SDEMDI). The experimental RP results show that the proposed method gives significantly more information about the velocity and displacement spectra and it is more stable and proportional than the SDEMDI method. The results of the proposed integration method are compared with vibration measurements obtained with an interferometer.

Separation of long-period components of ground motion and its impact on seismic response of long-period diagrid structures

The improved modified ensemble empirical mode decomposition (MEEMD) method is used to decompose the long-period ground motion, and the introduction of permutation entropy is used to eliminate the abnormal signal, which can avoid the phenomenon of mode aliasing in the traditional empirical mode decomposition method, so that the components of the long-period ground motion can be accurately separated. Taking the separated and reconstructed ground motions as input action, the influence of long-period components on the seismic response of diagrid structure of high-rise building is studied. The results demonstrate that the MEEMD method can accurately extract long-period ground motion components from long-period ground motion. Long-period ground motion contains abundant short-term energy with low-frequency, which is the main reason for the large seismic response of long-period structure. The seismic response of diagrid structure is closely related to the natural period of the structure and the spectrum characteristics of the ground motion.

Flow rate measurement of oil-gas-water wavy flow through a combined electrical and ultrasonic sensor

Water-based dispersed wavy flow, whereby the water with dispersed oil droplets moves along the bottom of the pipe and the gas concurrently on the top, is a frequently encountered horizontal oil-gas-water three-phase flow pattern in petroleum production and transportation. Accurate flow metering of each individual phase is significant to academic research and industrial flow assurance. Therefore, we present a combined electrical and ultrasonic sensor to simultaneously and non-intrusively acquire online flowing information. This information includes: the water fraction estimated by the normalized voltage from a conductance sensor, the gas fraction calculated by locating the gas-liquid interface through a pulse-wave ultrasonic sensor consisting of three transducers, and the coupled velocity of oil droplets and gas-liquid interface from Doppler shift signal acquired by a continuous wave ultrasonic Doppler sensor. Based on the limited flowing information obtained by multimodality sensors, a novel theoretical model is established based on the analysis of the momentum balance between different phases to solve phase fractions and phase actual velocities for calculating phase flow rates. To make the model closed and solvable, the interfacial velocity and oil velocity are respectively extracted by decoupling the Doppler shift signal through amplitude-aware permutation entropy and noise assisted multivariate empirical mode decomposition methods. Finally, the phase flow rates are calculated by the solutions of the established model. Dynamic experiments are performed to verify the effectiveness and accuracy of the proposed method and theoretical model. This study could provide a potential solution for non-intrusive phase flow metering of industrial three phase flow.

Research on Monthly Precipitation Prediction Based on the Least Square Support Vector Machine with Multi-Factor Integration

Accurate precipitation prediction is of great significance for regional flood control and disaster mitigation. This study introduced a prediction model based on the least square support vector machine (LSSVM) optimized by the genetic algorithm (GA). The model was used to estimate the precipitation of each meteorological station over the source region of the Yellow River (SRYE) in China for 12 months. The Ensemble empirical mode decomposition (EEMD) method was used to select meteorological factors and realize precipitation prediction, without dependence on historical data as a training set. The prediction results were compared with each other, according to the determination coefficient (R2), mean absolute errors (MAE), and root mean square error (RMSE). The results show that sea surface temperature (SST) in the Niño 1 + 2 region exerts the largest influence on accuracy of the prediction model for precipitation in the SRYE (RSST2= 0.856, RMSESST= 19.648, MAESST= 14.363). It is followed by the potential energy of gravity waves (Ep) and temperature (T) that have similar effects on precipitation prediction. The prediction accuracy is sensitive to altitude influences and accurate prediction results are easily obtained at high altitudes. This model provides a new and reliable research method for precipitation prediction in regions without historical data.

Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions

Abstract. Arctic sea ice drift motion affects the global material balance, energy exchange and climate change and seriously affects the navigational safety of ships along certain channels. Due to the Arctic's special geographical location and harsh natural conditions, observations and broad understanding of the Arctic sea ice motion are very limited. In this study, sea ice motion data released by the National Snow and Ice Data Center (NSIDC) were used to analyze the climatological, spatial and temporal characteristics of the Arctic sea ice drift from 1979 to 2018 and to understand the multiscale variation characteristics of the three major Arctic sea ice drift patterns. The empirical orthogonal function (EOF) analysis method was used to extract the three main sea ice drift patterns, which are the anticyclonic sea ice drift circulation pattern on the scale of the Arctic basin, the average sea ice transport pattern from the Arctic Ocean to the Fram Strait, and the transport pattern moving ice between the Kara Sea (KS) and the northern coast of Alaska. By using the ensemble empirical mode decomposition (EEMD) method, each temporal coefficient series extracted by the EOF method was decomposed into multiple timescale sequences. We found that the three major drift patterns have four significant interannual variation periods of approximately 1, 2, 4 and 8 years. Furthermore, the second pattern has a significant interdecadal variation characteristic with a period of approximately 19 years, while the other two patterns have no significant interdecadal variation characteristics. Combined with the atmospheric and oceanic geophysical variables, the results of the correlation analysis show that the first EOF sea ice drift pattern is mainly related to atmospheric environmental factors, the second pattern is related to the joint action of atmospheric and oceanic factors, and the third pattern is mainly related to oceanic factors. Our study suggests that the ocean environment also has a strong correlation with sea ice movement. Especially for some sea ice transport patterns, the correlation even exceeds atmospheric forcing.

Combined with Local Neighborhood Characteristics and Remote Sensing Image Fusion Method of C-BEMD

For the sake of ameliorate the high resolution recognition capacity building remote sensing images, a remote sensing image fusion method based on local neighborhood characteristics and C-BEMD is advanced. The building remote sensing image acquisition model and the building remote sensing image picture element edge feature detection model are designed. The wavelet multi-scale denoising method is used to suppress the fuzzy spread of picture element feature points between image residual units, extract the geometric feature points of image sequence, and process the building remote sensing image block by block. The global residual learning and message fusion of building remote sensing image are implemented. The local neighborhood feature matching method is used to reconstruct the building remote sensing image region. Combined with the C-BEMD empirical mode decomposition method, the building remote sensing image fusion and feature point matching in affine region are implemented, and the block image template matching method is used to realize the automatic fusion and recognition of building remote sensing image. Simulation results show that this method has high precision in constructing remote sensing image fusion and good positioning performance in constructing remote sensing image feature points.

An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition

Rotating machinery refers to machinery that executes specific functions mainly relying on their rotation. They are widely used in engineering applications. Bearings and gearboxes play a key role in rotating machinery, and their states can directly affect the operation status of the whole rotating machinery. Accurate fault detection and judgment of bearing, gearbox, and other key parts are of great significance to the rotating machinery’s normal operation. A new fault feature extraction algorithm for rotating machinery called Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy (ImvMAAPE) is proposed in this paper, and the application of an improved coarse-grained method in fault feature extraction of multichannel signals is realized in this method. This algorithm is combined with the Uniform Phase Empirical Mode Decomposition (UPEMD) method and the t-distributed Stochastic Neighbor Embedding (t-SNE) method, forming a new time-frequency multiscale feature extraction method. Firstly, the multichannel vibration signals are decomposed adaptively into sets of Intrinsic Mode Functions (IMFs) using UPEMD; then, the IMF components containing the main fault information are screened by correlation analysis to get the reconstructed signals. The ImvMAAPE values of the reconstructed signals are calculated to generate the initial high-dimensional fault features, and the t-SNE method with excellent nonlinear dimensionality reduction performance is then used to reduce the dimensionality of the initial high-dimensional fault feature vectors. Finally, the low dimensional feature vectors with high quality are input to the random forest (RF) classifier to identify and judge the fault types. Experiments were conducted to verify whether this method has higher accuracy and robustness than other methods.

Audio Information Camouflage Detection for Social Networks

Sending camouflaged audio information for fraud in social networks has become a new means of social networks attack. The hidden acoustic events in the audio scene play an important role in the detection of camouflaged audio information. Therefore, the application of machine learning methods to represent hidden information in audio streams has become a hot issue in the field of network security detection. This study proposes a heuristic mask for empirical mode decomposition (HM-EMD) method for extracting hidden features from audio streams. The method consists of two parts: First, it constructs heuristic mask signals related to the signal’s structure to solve the modal mixing problem in intrinsic mode function (IMF) and obtains a pure IMF related to the signal’s structure. Second, a series of hidden features in environment-oriented audio streams is constructed on the basis of the IMF. A machine learning method and hidden information features are subsequently used for audio stream scene classification. Experimental results show that the hidden information features of audio streams based on HM-EMD are better than the classical mel cepstrum coefficients (MFCC) under different classifiers. Moreover, the classification accuracy achieved with HM-EMD increases by 17.4 percentage points under the three-layer perceptron and by 1.3% under the depth model of TridentResNet. The hidden information features extracted by HM-EMD from audio streams revealed that the proposed method could effectively detect camouflaged audio information in social networks, which provides a new research idea for improving the security of social networks.

Probing the impact of Solar System dynamics on Holocene North Atlantic cold events at the millennial-scale

In general, although the climate during the Holocene has been warm, nine well-recognized cold events with an average time interval of more than one thousand years have been highlighted. The forcing mechanism of these cold events with millennial-scale cycles is still widely discussed. In the present study, two different Solar System dynamical processes collectively driven by all eight planets were considered together: the solar inertial motion and the combined planetary tidal force acting on the Sun. In this context, an attempt was made to analyze their effects on solar activity and Holocene North Atlantic cold events at the millennial-scale. These two Solar System dynamical processes were proxied by the distance between the Sun and the Solar System barycenter (DS–S) and the intensity of the combined planetary tidal force (IP–S), respectively. Using the ensemble empirical mode decomposition (EEMD) method, time series data of Solar System dynamics proxies (i.e., DS–S, and IP–S), solar activity proxies (i.e., 14C and 10Be), and a proxy of Holocene North Atlantic cold events (i.e., hematite-stained grains) were decomposed into several intrinsic mode functions (IMFs). After extracting IMF components containing millennial-scale cycles, a correlation analysis was performed. As a result, it was found that the solar inertial motion had ~2,300- and ~1,000-year cycles and the combined planetary tidal force had a ~1,500-year cycle, while the solar activity and Holocene North Atlantic cold events proxies had ~2,300-, ~1,500-, and ~1,000-year cycles, respectively. The correlation analysis revealed that the millennial-scale periodic components of the two Solar System dynamics proxies were largely correlated with that of solar activity and Holocene North Atlantic cold events, suggesting that the solar inertial motion and combined planetary tidal force may work together to impact solar activity, and thus the climate in the North Atlantic. Consequently, solar activity was weaker and the North Atlantic temperature was cooler when the Sun was far from the Solar System barycenter and/or the combined planetary tidal force was weakened. This might indicate the involvement of Solar System dynamics on Holocene North Atlantic cold events at the millennial-scale.