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Preterm birth prediction from electrohysterogram using multivariate empirical mode decomposition

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2025年 1-14页

作者： Cui, Jiawen Zhang, Xu Li, Xinhui Luo, Xuanyu Chen, Xiang Yin, Zongzhi Univ Sci & Technol China Sch Microelect Hefei 230026 Anhui Peoples R China Anhui Med Univ Dept Obstet & Gynecol Affiliated Hosp 1 Hefei 230022 Anhui Peoples R China

Electrohysterogram (EHG) is an electrophysiological signal describing uterine contractions that can be non-invasively measured on maternal abdominal surface. This signal contains vital physiological and pathological information for assessing delivery abnormalities, such as preterm birth. However, extracting information that effectively characterizes the association with abnormal delivery from the weak EHG signal is challenging. We present a preterm birth predicting method using multivariate empirical mode decomposition (MEMD) algorithm that adaptively decomposes multichannel EHG signals into different intrinsic mode functions (IMFs). MEMD maintains spectral consistency across channels and avoids mode-mixing problems across IMFs due to its powerful fine-grained signal structure decoupling capability. On this basis, a total of 180 features were extracted from the IMFs and the final eight features were chosen using a two-step feature selection algorithm. A support vector machine (SVM) classifier was employed for decision-making. Specifically, cost-sensitive algorithm was used to solve the data imbalance problem. The proposed method was evaluated using 300 EHG recordings in TPEHG database. The results show that our method outperforms other state-of-the-art methods in terms of sensitivity (85.16%), specificity (96.54%), F1score\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${F1}_{\text{score}}$$\end{document} (91.04%), accuracy (94.36%), and AUC (97.31%). This study provides a powerful tool with wide applications for preterm birth risk diagnosis in clinical obstetric.

关键词： Preterm birth prediction Electrohysterogram Feature selection Data balancing multivariate empirical mode decomposition

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Noise-Assisted Graph multivariate empirical mode decomposition With Non-Uniform Projections

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第4期72卷 1707-1717页

作者： Sun, Xuandi Nassif, Roula Richard, Cedric Wang, Haiyan Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Univ Cote Azur Lab Lagrange CNRS F-06103 Nice France Univ Cote Azur CNRS Lab Informat Signaux & Syst I3S F-06903 Sophia Antipolis France Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China

Latest advances in multi-agent technology and hardware architecture design have made multivariate or multichannel data, often supported by graphs or networks, ubiquitous in recent scientific and engineering applications. Examples include smart grids management and sensor networks monitoring to cite a few. multivariate empirical mode decomposition (MEMD), as a fully data-driven technique, has been shown to be effective in the multiscale analysis of non-stationary signals across multiple channels. However, it still lacks the capability to capture the dependency structure of signals over channels when supported by a graph, which limits the relevance of the provided analyses. This work aims to extend MEMD to temporal graph signals. To achieve this, non-uniform projections are processed to preserve smoothness relative to the topology of the graph. A noise-assisted mechanism is also proposed in order to adapt to the randomness of signals on vertices, eliminating mode mixing and misalignment phenomena. To further demonstrate the performance of the proposed graph multivariate empirical mode decomposition (GMEMD), and in particular of its noise-assisted counterpart, we validate its mode alignment property among same-index intrinsic mode functions and its efficacy as a filter bank. Simulations on both synthetic temporal graph signals and real-world electroencephalogram data support the analysis.

关键词： Vectors empirical mode decomposition Standards Time-frequency analysis Sun Topology Laplace equations Brain modeling Analytical models Time series analysis Multi-agent technology temporal graph signal processing multivariate empirical mode decomposition non-uniform projections mode mixing misalignment noise-assisted mechanism filter bank

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Forecasting stock market time series through the integration of bee colony optimizer and multivariate empirical mode decomposition with extreme gradient boosting regression

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 148卷

作者： Liu, Xuefeng Wu, Zhixin Xin, Jiayue Harbin Univ Sch Econ & Management Harbin 150086 Heilongjiang Peoples R China Zhejiang Univ Finance & Econ China Res Inst Regulat & Publ Policy Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Finance & Econ Coll Business Adm Dongfang Coll Haining 314408 Zhejiang Peoples R China Zhejiang Univ Finance & Econ Coll Business Adm Hangzhou 310018 Zhejiang Peoples R China Capital Univ Econ & Business Sch Stat Beijing 100026 Peoples R China

Stock price prediction is essential for the optimization of investment strategies, the mitigation of risks, and the facilitation of informed decision-making. Accurate forecasting is exceedingly difficult due to the nonlinear, nonstationary, and volatile nature of stock prices. This complexity is frequently not adequately addressed by conventional methods, underscoring the necessity of sophisticated hybrid models. This study uses stock price data from the Standard & Poor's 500 Index to develop a novel hybrid model, multivariate empirical mode decomposition-Artificial Bee Colony-Extreme Gradient Boosting Regression. Extreme Gradient Boosting Regression captures intricate patterns in the data, Artificial Bee Colony optimizes the hyperparameters of Extreme Gradient Boosting Regression to enhance model robustness, and multivariate empirical mode decomposition decomposes complex financial time-series data into manageable intrinsic mode functions. Close price, Momentum, Simple Moving Average, Moving Average Convergence Divergence, Relative Strength Index, and Trading volume comprise the dataset. These features are indispensable for identifying both short-term fluctuations and long-term trends. The presented model is significantly more effective than traditional models, as evidenced by its test set coefficient of determination of 0.9914. The proposed model's robustness is confirmed by comprehensive 5-fold cross-validation and ablation studies, which also emphasize the significance of its integrated components. Furthermore, the model's adaptability is further illustrated by its ability to generalize to other markets, as evidenced by its coefficient of determination values exceeding 0.99 on three other indexes. These results underscore the potential of artificial intelligence-driven hybrid models to enhance stock price forecasting, offering useful insights for policymakers, financial analysts, and investors.

关键词： Financial market Stock price Standard and Poor's 500 Artificial bee colony Extreme gradient boosting multivariate empirical mode decomposition

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multivariate empirical mode decomposition-based structural damage localization using limited sensors

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JOURNAL OF VIBRATION AND CONTROL 2022年第15-16期28卷 2155-2167页

作者： Sony, Sandeep Sadhu, Ayan Western Univ Dept Civil & Environm Engn 1151 Richmond St London ON N6A 3K7 Canada

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time-frequency methods to delineate such minor global damage.

关键词： Structural health monitoring damage localization multivariate empirical mode decomposition damage index limited sensors

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multivariate empirical mode decomposition applied to the estimation of the decay ratio and out-of-phase oscillations in BWRs

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PROGRESS IN NUCLEAR ENERGY 2015年 78卷 163-172页

作者： Prieto-Guerrero, Alfonso Espinosa-Paredes, Gilberto Abel Laguna-Sanchez, Gerardo Univ Autonoma Metropolitana Div Ciencias Basicas & Ingn Mexico City 09340 DF Mexico Univ Autonoma Metropolitana Div Ciencias Basicas & Ingn Lerma 52005 Estado De Mexic Mexico

This work presents a novel method based on the multivariate empirical mode decomposition (MEMD) to estimate the Decay Ratio (DR) and the apparition of a possible out-of-phase oscillation in Boiling Water Reactors (BWRs). The MEMD algorithm, have the potential to find common oscillatory modes within multivariate data called Intrisic mode Functions (IMF). Based on this fact, we implement the MEMD technique to find the common oscillatory mode (density wave) in BWRs considering the measurements obtained in each Local Power Range Monitor (LPRM) through the entire core. These IMFs, analyzed simultaneously in time, permit to obtain an estimation of the DR and;also give some considerations about an incipient out-of-phase oscillation through the phase measured between two specific LPRMs. Effectiveness of the proposed method is demonstrated on a specific challenging case from the Forsmark stability benchmark. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： multivariate empirical mode decomposition Forsmark stability benchmark Decay ratio Out-of-phase oscillations Boiling water reactors

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multivariate empirical mode decomposition Derived Multi-Scale Spatial Relationships between Saturated Hydraulic Conductivity and Basic Soil Properties

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CLEAN-SOIL AIR WATER 2015年第6期43卷 910-918页

作者： She, Dongli Zheng, Jiaxing Shao, Ming'an Timm, Luis Carlos Xia, Yongqiu Hohai Univ Coll Water Conservancy & Hydropower Engn Key Lab Efficient Irrigat Drainage & Agr Soil Wat Minist Educ Nanjing Jiangsu Peoples R China Chinese Acad Sci Inst Soil Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Jiangsu Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Ecosyst Network Observat & Modeling Beijing Peoples R China Univ Fed Pelotas Fac Agron Dept Rural Engn Pelotas RS Brazil

Saturated hydraulic conductivity (K-s) is affected by various factors operating at different scales. This study identified the multi-scale spatial relationships between Ks and selected basic soil properties (soil organic matter [SOM], clay, silt, and sand contents, and bulk density) along two landscape transects (with various soil textures and land use covers) on the Loess Plateau. multivariate empirical mode decomposition (MEMD) yielded four different intrinsic mode functions (IMFs) for the multivariate data series of each transect according to the scale of occurrence. The dominant scales in terms of explained variance of Ks were IMF1 (scale: 403 m) for transect 1, and IMF1 and IMF2 (scale: 407 and 775 m) for transect 2. The multi- scale correlation between Ks and soil properties was more complex for transect 1 due to a more fragmented landscape. For each IMF or residue, Ks was predicted using the identified factors that significantly affected it at that IMF scale or residue. The summation of the four predicted IMFs and the residue predicted Ks at the measurement scale, and was more accurate than predictions based on simple multiple linear regressions between Ks and the other soil properties. Soil particle size components were the main contributors in explaining Ks variability for both landscape transects, mostly due to their contributions from IMF1;however, SOM was also a major contributor for transect 2, mainly due to contributions from IMF2. Using MEMD has great potential in characterizing scale- dependent spatial relationships between soil properties in complicated landscape ecosystems.

关键词： Intrinsic mode functions Landscape transects multivariate empirical mode decomposition Scale Soil hydraulic property

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multivariate empirical mode decomposition BASED SIGNAL ANALYSIS AND EFFICIENT-STORAGE IN SMART GRID

MULTIVARIATE EMPIRICAL MODE DECOMPOSITION BASED SIGNAL ANALY...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Liu, Liu Albright, Austin Rahimpour, Alireza Guo, Jiahui Qi, Hairong Liu, Yilu Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Oak Ridge Natl Lab Elect & Elect Syst Res Div Oak Ridge TN USA

ISBN: (纸本)9781509045457

Wide-area-measurement systems (WAMSs) are used in smart grid systems to enable the efficient monitoring of grid dynamics. However, the overwhelming amount of data and the severe contamination from noise often impede the effective and efficient data analysis and storage of WAMS generated measurements. To solve this problem, we propose a novel framework that takes advantage of multivariate empirical mode decomposition (MEMD), a fully data-driven approach to analyzing non-stationary signals, dubbed MEMD based Signal Analysis (MSA). The frequency measurements are considered as a linear superposition of different oscillatory components and noise. The low-frequency components, corresponding to the long-term trend and inter-area oscillations, are grouped and compressed by MSA using the mean shift clustering algorithm. Whereas, higher-frequency components, mostly noise and potentially part of high-frequency inter-area oscillations, are analyzed using Hilbert spectral analysis and they are delineated by statistical behavior. By conducting experiments on both synthetic and real-world data, we show that the proposed framework can capture the characteristics, such as trends and inter-area oscillation, while reducing the data storage requirements.

关键词： Data Characterization Big Data multivariate empirical mode decomposition Mean Shift Clustering

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multivariate empirical mode decomposition Based EMG Signal Analysis For Smart Prosthesis 26

Multivariate Empirical Mode Decomposition Based EMG Signal A...

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Onay, Fatih Mert, Ahmet Bursa Tekn Univ Mekatron Muhendisligi Bolumu Bursa Turkey

ISBN: (纸本)9781538615010

Electromyography (EMG) signals are successfully used for human-robot interaction with biomedical applications. One of the basic components of many modern prosthesis is the myoelectric control system which controls prosthetic movements through EMG signals. In this study, multivariate empirical mode decomposition (MEMD) based signal processing and analysis of EMG signals was investigated in the decision making process of smart hand proshesis movements of transradial amputees. Due to MEMD's non-linear and non-stationary signal processing capability, the obtained MEMD-based features are intended to increase the performance of the controlled prosthesis using multi-channel EMG signals. The MEMD-based features obtained through the EMG signals recorded for 6 positions from 9 transradial amputees were classified by the nearest neighbors and decision tree algorithms and an average of 77% (up to 100% for some amputees) accuracy was obtained for a maximum of 9 amputees.

关键词： Smart prosthesis Electromyography multivariate empirical mode decomposition Sample entropy

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Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms

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JOURNAL OF HYDROLOGY 2019年 576卷 164-184页

作者： Ali, Mumtaz Deo, Ravinesh C. Maraseni, Tek Downs, Nathan J. Univ Southern Queensland Sch Agr Computat & Environm Sci Ctr Appl Climate Sci Springfield Qld 4300 Australia Univ Southern Queensland Ctr Sustainable Agr Syst Inst Life Sci & Environm Springfield Qld 4300 Australia Deakin Univ Deakin SWU Joint Res Ctr Big Data Sch Informat Technol 221 Burwood Highway Burwood Vic 3125 Australia

New and improved drought models based on the World Meteorological Organization approved Standardized Precipitation Index, principally at multiple timescale horizons, are providing significant benefits to the hydrological community, by its widespread acceptance in the sub-field of water resources management, sustainable water use and precision agriculture. In this research paper, the existing challenges faced by a drought forecasting model trained at multiple time-scales are resolved where a new multi-phase, multivariate empirical mode decomposition model integrated with simulated annealing and Kernel ridge regression algorithms (Le., MEMD-SAKRR) is designed to attain significantly accurate drought forecasts for 3 agricultural sites (Le., Faisalabad, Islamabad and Jhelum, located in Pakistan). Utilizing the multi-scalar Standardized Precipitation Index (SPI) time series as a target variable for characterization of drought, twelve multivariate datasets (derived from statistically significant lagged combinations of precipitation, temperature & humidity), that are enriched with eight synoptic-scale climate mode indices and periodicity, are utilized in designing a new drought model. The study constructs a hybrid MEMD-SA-KRR model, where firstly, the data are partitioned into their respective training and testing subsets after creating historically lagged SPI at timescale (t-1). Secondly, the MEMD algorithm is conditioned to demarcate multivariate climate indices from their training and testing sets, separately, into their decomposed intrinsic mode functions (IMFs) and residues. Thirdly, the SA method is employed to decide the most suitable IMFs. Finally, the KRR algorithm is applied to the selected IMFs to forecast multi-scaler SPI, at 1-, 3-, 6- and 12-monthly forecast horizon. The results are benchmarked with Random Forest, integrated with MEMD and SA to develop the MEMD-SA-RF equivalent model. The multi-phase MEMD-SA-KRR model is tested geographically in Pakistan, rev

关键词： Hybrid drought Forecast model multivariate empirical mode decomposition Simulated annealing Kernel ridge regression

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Classification of non-motor cognitive task in EEG based brain-computer interface using phase space features in multivariate empirical mode decomposition domain

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2018年第0期39卷 378-389页

作者： Dutta, Suman Singh, Mandeep Kumar, Amod Thapar Univ Dept Elect & Instrumentat Engn Patiala Punjab India Cent Sci Instruments Org Chandigarh India

The purpose of this research paper is to present a new framework for EEG feature extraction based on the combination of multivariate empirical mode decomposition (MEMD) and phase space reconstruction (PSR) for classifying a small set of non-motor cognitive task EEG signals in mental task based multitask brain computer interface(BCI) system. Our proposed approach employed phase space analysis of the intrinsic mode functions (IMFs) generated by MEMD based decomposition of the six channels EEG signals. The combination of two powerful signal processing techniques i.e. MEMD with phase space reconstruction (PSR) enabled us to characterize the nonlinear and non-stationary nature of the dynamics underlying a particular cognitive task more accurately. Our proposed approach consists of three stages, in the first stage;the application of MEMD to multichannel EEG data gave rise to adaptive i.e. data driven decomposition of the multivariate time series data into a set of IMF groups. All the member IMFs within a group have common oscillatory frequency but different amplitude and cortical origin. In the second stage, a small subset of IMF groups was selected according to their task correlation factor and subsequently represented in the two dimensional phase space through their trajectory matrices. In the third stage, largest singular values of the trajectory matrices corresponding to a subset of sensitive IMFs were employed for forming the feature vectors. Finally, the extracted feature vectors were fed to a least square support vector machine (LS-SVM) classifier for binary i.e. pair wise classification of these mental task EEG signals. With the new feature vectors, it is shown that LS-SVM with RBF kernel provides accuracy of 83.33% in classifying between mental arithmetic and mental letter composing. The performance of this classifier was evaluated on various parameters such as accuracy, specificity and sensitivity. The classification results show the potential of the proposed ap

关键词： Electroencephalogram multivariate empirical mode decomposition Intrinsic mode function Phase space reconstruction Singular value decomposition Least square support vector machine

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