A Frequency Estimation Scheme Based on Gaussian Average Filtering Decomposition and Hilbert Transform: With Estimation of Respiratory Rate as an Example.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert-Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert-Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland-Altman analysis.

Crisis Periods Detection on NASDAQ Index Via EEMD-AE

It is proposed to identify the beginning and end of the SARS-CoV-2 and subprime crises on the NASDAQ. The EEMD was used to decompose the index into consecutive series with the same number of components and their correlation coefficients were calculated, the power spectrum of the original series was also analyzed. Signals of instability associated with changes in both the components' correlations and the NASDAQ spectrum were identified. It is recommended to apply the procedure on other series and other crises;likewise, the method is based on the detection of discrepancies, thus being a monitoring tool, but not one of quantitative forecasts. The originality of the work lies in the use of the modified EEMD for the decomposition of consecutive series in the same number of components, and the use of the correlation coefficient between components and the spectrum of the original series as measures of system stability. The approach proved to be useful for identifying and anticipating large changes in the behavior of a time series. © 2022 The authors.

A New Container Throughput Forecasting Paradigm under COVID-19

COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. This paper proposes a novel decomposition-ensemble forecasting method to forecast container throughput under the impact of major events. Combining this with change-point analysis and empirical mode decomposition (EMD), this paper uses the decomposition-ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, we tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. To validate the proposed method, we apply it to a forecast of the container throughput of Shanghai port. The results show that the proposed forecasting model significantly outperforms its rivals, including EMD-SVR, SVR, and ARIMA.

An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19.

At present, the Corona Virus Disease 2019 (COVID-19) is ravaging the world, bringing great impact on people's life safety and health as well as the healthy development of economy and society, so the research on the prediction of the development trend of the epidemic is crucial. In this paper, we focus on the prevention and control of epidemic using the relevant technologies in the field of artificial intelligence and signal analysis. With the unknown principle of epidemic transmission, we first smooth out the complex and variable epidemic data through the empirical mode decomposition model to obtain the change trends of epidemic data at different time scales. On this basis, the change trends under different time scales are trained using an extreme learning machine to obtain the corresponding prediction values, and finally the epidemic prediction results are obtained by fitting through Adaptive Network-based Fuzzy Inference System. The experimental results show that the algorithm has good learning ability, especially in the prediction of time-series sequences can guarantee the accuracy rate while having low time complexity. Therefore, this paper not only plays a theoretical support for epidemic prevention and control, but also plays an important role in the construction of public emergency health system in the long run.

The Financial Market in China under the COVID-19

The main aim of this study is to investigate the effects of COVID-19 on financial markets in China. Results of correlation analysis indicate that higher financial correlation among provinces emerged after the official announcement regarding COVID-19 in China. The Minimum Spanning Tree (MST) results after the pandemic announcement denote that Shanghai, Beijing, Jiangsu, Zhejiang, and Chongqing become the new cores, and the overall linking type exhibits cluster mode, which is varied from the intertwined connection mode. In addition, through Ensemble Empirical Mode Decomposition (EEMD) and Wavelet analysis, we found that financial markets in China are more susceptible to unexpected incidents.

Using the hybrid EMD-BPNN model to predict the incidence of HIV in Dalian, Liaoning Province, China, 2004-2018.

BACKGROUND: Acquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with high mortality caused by HIV (human immunodeficiency virus, and up to now there are no curable drugs or effective vaccines. In order to understand AIDS's development trend, we establish hybrid EMD-BPNN (empirical modal decomposition and Back-propagation artificial neural network model) model to forecast new HIV infection in Dalian and to evaluate model's performance. METHODS: The monthly HIV data series are decomposed by EMD method, and then all decomposition results are used as training and testing data to establish BPNN model, namely BPNN was fitted to each IMF (intrinsic mode function) and residue separately, and the predicted value is the sum of the predicted values from the models. Meanwhile, using yearly HIV data to established ARIMA and using monthly HIV data to established BPNN, and SARIMA (seasonal autoregressive integrated moving average) model to compare the predictive ability with EMD-BPNN model. RESULTS: From 2004 to 2017, 3310 cases of HIV were reported in Dalian, including 101 fatal cases. The monthly HIV data series are decomposed into four relatively stable IMFs and one residue item by EMD, and the residue item showed that the incidence of HIV increases firstly after declining. The mean absolute percentage error value for the EMD-BPNN, BPNN, SARIMA (1,1,2) (0,1,1)12 in 2018 is 7.80%, 10.79%, 9.48% respectively, and the mean absolute percentage error value for the ARIMA (3,1,0) model in 2017 and 2018 is 8.91%. CONCLUSIONS: The EMD-BPNN model was effective and reliable in predicting the incidence of HIV for annual incidence, and the results could furnish a scientific reference for policy makers and health agencies in Dalian.

A New CNN-Based Method for Short-Term Forecasting of Electrical Energy Consumption in the Covid-19 Period: The Case of Turkey

This study proposes a new convolutional neural network (CNN) method with an input-signal decomposition algorithm. With the proposed CNN architecture, hourly electricity consumption data for the Covid-19 period in Turkey were used as input data, and the short-term electricity consumption was forecasted. The input data were decomposed into its subcomponents using a signal decomposition process called Empirical Mode Decomposition (EMD). To extract the deep features, all input data were transformed into 2D feature maps and fed into the CNN. The obtained results were compared with the pre-trained models GoogleNet, AlexNet, SqueezeNet, and ResNet18. Model-wise comparisons showed that the proposed method had the highest correlation coefficient (R) and lowest root mean square error (RMSE) and mean absolute error (MAE) values for 1-h, 2-h, and 3-h. The mean R-values of the proposed method were 95.6%, 95.2%, and 94.0% for 1h, 2h and 3h ahead, respectively. The mean RMSE values were 8.2%, 8.7%, and 10.2% for 1h, 2h and 3h ahead, respectively. The experimental results confirm that the proposed method outperforms other pretrained methods despite its simpler structure. © 2013 IEEE.

A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition.

The outbreak of the SARS-CoV-2/Covid-19 virus in 2019-2020 has made the world look for fast and accurate detection methods of the disease. The most commonly used tools for detecting Covid patients are Chest-X-ray or Chest-CT-scans of the patient. However, sometimes it's hard for the physicians to diagnose the SARS-CoV-2 infection from the raw image. Moreover, sometimes, deep-learning-based techniques, using raw images, fail to detect the infection. Hence, this paper represents a hybrid method employing both traditional signal processing and deep learning technique for quick detection of SARS-CoV-2 patients based on the CT-scan and Chest-X-ray images of a patient. Unlike the other AI-based methods, here, a CT-scan/Chest-X-ray image is decomposed by two-dimensional Empirical Mode Decomposition (2DEMD), and it generates different orders of Intrinsic Mode Functions (IMFs). Next, The decomposed IMF signals are fed into a deep Convolutional Neural Network (CNN) for feature extraction and classification of Covid patients and Non-Covid patients. The proposed method is validated on three publicly available SARS-CoV-2 data sets using two deep CNN architectures. In all the databases, the modified CT-scan/Chest-X-ray image provides a better result than the raw image in terms of classification accuracy of two fundamental CNNs. This paper represents a new viewpoint of extracting preprocessed features from the raw image using 2DEMD.

Co-crystallization and structure determination: An effective direction for anti-SARS-CoV-2 drug discovery.

Safer and more-effective drugs are urgently needed to counter infections with the highly pathogenic SARS-CoV-2, cause of the COVID-19 pandemic. Identification of efficient inhibitors to treat and prevent SARS-CoV-2 infection is a predominant focus. Encouragingly, using X-ray crystal structures of therapeutically relevant drug targets (PLpro, Mpro, RdRp, and S glycoprotein) offers a valuable direction for anti-SARS-CoV-2 drug discovery and lead optimization through direct visualization of interactions. Computational analyses based primarily on MMPBSA calculations have also been proposed for assessing the binding stability of biomolecular structures involving the ligand and receptor. In this study, we focused on state-of-the-art X-ray co-crystal structures of the abovementioned targets complexed with newly identified small-molecule inhibitors (natural products, FDA-approved drugs, candidate drugs, and their analogues) with the assistance of computational analyses to support the precision design and screening of anti-SARS-CoV-2 drugs.